C. elegans strains were cultured at 20 °C on standard Nematode Growth Medium seeded with OP50 Escherichia coli70. On day 1 of adulthood, worms were transferred to plates containing OP50 E. coli (or HT115 E. coli for RNAi experiments) supplemented with 100 μg ml−1 5-fluoro-2′-deoxyuridine to prevent progeny development, except in lifespan assays. All experiments were conducted using hermaphrodite worms, and the age of the worms is indicated in the corresponding figures and figure legends.
WT (N2) and AM141 (rmIs133[unc-54p::Q40::YFP]) strains were obtained from the Caenorhabditis Genetics Center (CGC), supported by the National Institutes of Health Office of Research Infrastructure Programs (P40 OD010440). RB751 (eps-8(ok539)) was generated by the C. elegans Gene Knockout Consortium and acquired from the CGC. AM23 (rmIs298[F25B3.3p::Q19::CFP]) and AM716 (rmIs284[F25B3.3p::Q67::YFP]) strains were gifted by Richard I. Morimoto24. MAH602 (sqIs61[vha-6p::Q44::YFP + rol-6(su1006)]) was provided by Malene Hansen71. ZM5838 (hpIs223[rgef-1p::FUSWT::GFP]), ZM5844 (hpIs233[rgef-1p::FUSP525L::GFP]) and ZM5842 (hpIs228[rgef-1p::FUSR522G::GFP]) were provided by Peter St. George-Hyslop45. CK405(Psnb-1::TDP-43WT,myo-2p::dsRED) and CK423 (Psnb-1::TDP-43M337V,myo-2p::dsRED) were provided by Brian C. Kraemer33.
From these strains, we generated NFB2862 (Psnb-1::TDP-43WT,myo-2p::dsRED;juIs76[unc-25p::GFP + lin-15(+)]II) and NFB2863 (Psnb-1::TDP-43M337V,myo-2p::dsRED;juIs76[unc-25p::GFP + lin-15(+)]II). NFB2858 (rmIs298[F25B3.3p::Q19::CFP];otIs549[unc-25p::unc-25(partial)::mChopti::unc-54 3′ untranslated region (UTR) + pha-1(+)];him-5(e1490)V), NFB2859 (rmIs284[F25B3.3p::Q67::YFP];otIs549[unc-25p::unc-25(partial)::mChopti::unc-54 3′ UTR + pha-1(+)];him-5(e1490)V), NFB2860 (hpIs223[rgef-1p::FUSWT::GFP];otIs549[unc-25p::unc-25(partial)::mChopti::unc-54 3′ UTR + pha-1(+)];him-5(e1490)V) and NFB2861 (hpIs233[rgef-1p::FUSP525L::GFP];otIs549[unc-25p::unc-25(partial)::mChopti::unc-54 3′ UTR + pha-1(+)];him-5(e1490)V) were generated by crossing the respective polyQ and FUS-expressing strains with the OH13526 strain72. For RNAi in the neurons of polyQ67 worms, we used the DVG196 strain (rmIs284[F25B3.3p::Q67::YFP];sid-1(pk3321)V;uIs69[pCFJ90(myo-2p::mCherry) + unc-119p::sid-1]).
Worms expressing endogenous WT EPS-8::3xHA (VDL05, eps-8(syb2901)IV) or mutant EPS-8(K524R/K583R/K621R::3×HA) (VDL06, eps-8(syb2901, syb3149)IV) were previously generated via CRISPR–Cas9 (ref. 14). The strains DVG344 (rmIs284[pF25B3.3::Q67::YFP]);eps-8(syb2901) and DVG363 (rmIs133[unc-54p::Q40::YFP]);eps-8(syb2901) were generated by crossing VDL05 with AM716 and AM141, respectively. DVG345 (rmIs284[pF25B3.3::Q67::YFP]);eps-8(syb2901, syb3149) and DVG364 (rmIs133[unc-54p::Q40::YFP]);eps-8(syb2901, syb3149) were generated by crossing VDL06 to AM716 and AM141, respectively. These strains were validated by sequencing using the following primers: eps-8(syb2901): 5′-TTTGTTCGAAGCATGAACGA-3′ and 5′-AGCAGCCCCTGAAATAGTGA-3′; eps-8(syb2901, syb3149): 5′-AACGAGCTAGCAATCCGAAA-3′ and 5′-AGTGCTCTGCCGTCATTAAT-3′. DVG365 (rmIs284[pF25B3.3::Q67::YFP];eps-8(ok539)) was generated by crossing RB751 to AM716. The strain was outcrossed two times to AM716 and validated by polymerase chain reaction with 5′-TCTCCACCACCACAACGTAA-3′ and 5′-GCGGAGCAACTCTTCCATAG-3′ primers.
RNAi constructs
Adult worms were fed HT115 E. coli carrying either an empty control vector (L4440) or vectors expressing double-stranded RNAi. The RNAi constructs targeting eps-8, ifb-2, jnk-1, kgb-1, mig-2 and otub-3 were obtained from the Vidal library. The csn-6, F07A11.4, math-33, rac-2, usp-4, usp-5 and usp-48 RNAi constructs were obtained from the Ahringer library. All RNAi constructs were sequence verified. The RNAi sequences are listed in Supplementary Table 2.
Lifespan assay
Larvae were synchronized using the egg-laying protocol and grown on OP50 E. coli at 20 °C until day 1 of adulthood. Adult hermaphrodites were then transferred to plates with HT115 E. coli carrying either an empty vector or RNAi constructs for lifespan assays. All lifespan assays were performed at 20 °C. Each condition included 96 worms, scored daily or every other day73. Worms that were lost, burrowed into the medium, had a protruding vulva or underwent bagging were censored73.
Nose touch assay
Age-synchronized worms were assessed for nose touch response as previously described74,75,76. In brief, worms were placed on a thin bacterial lawn, and an eyelash pick was positioned in front of a forward-moving animal. A lack of response was recorded when the worm continued moving forward to crawl under or over the pick. For each condition, 30–40 animals were tested by monitoring the number of responses to a total of 10 gentle eyelash touches.
Chemotaxis assay
Freshly prepared agar plates (2% agar, 5 mM KPO4 (pH 6.0), 1 mM CaCl2, 1 mM MgSO4) were divided into four equal quadrants, along with an inner circle measuring approximately 1 cm across diagonally. A test solution (0.5% benzaldehyde (Sigma-Aldrich, B1334) in ethanol + 0.25 M sodium azide) and a control solution (ethanol + 0.25 M sodium azide) were added to two opposing diagonal quadrants. On the indicated days of adulthood (as shown in the corresponding figures), worms were collected in S-Basal medium, washed three times to remove residual bacteria and placed at the center of the chemotaxis plate. The plates were sealed with parafilm and incubated at 20 °C for 90 minutes. The number of worms in each quadrant was counted, excluding those that did not cross the inner circle. The chemotaxis index was calculated using the following formula: chemotaxis index = ((number of animals in test quadrants) − (number of animals in control quadrants)) / total number of animals77.
Motility assays
C. elegans were synchronized on OP50 E. coli using the egg-laying method and grown until day 1 of adulthood and then randomly transferred to plates with HT115 E. coli containing either empty vector or RNAi for the remainder of the experiment. For experiments with Ub-less EPS-8 mutants or DUB inhibitor treatment, worms were instead transferred to fresh plates containing OP50 E. coli. On the indicated day of adulthood (as shown in the corresponding figures), worms were randomly picked and transferred to a drop of M9 buffer, allowing 30 seconds for recovery24. Body bends were then recorded for 30 seconds and analyzed using ImageJ software (version 1.53k) with the wrMTrck plugin (https://www.phage.dk/plugins/)78,79. The locomotion velocity data were used to calculate body bends per second.
Microscopy
For imaging GABAergic neurons, fluorescent reporter worms were anesthetized with a drop of 0.5 M sodium azide (Sigma-Aldrich, 26628-22-8) on 4% agarose pads (diluted in distilled water) placed over a standard microscope glass slide (Rogo-Sampaic, 11854782). These preparations were sealed with 24 × 60-mm coverslips (RS France, BPD025). To score the number of GABAergic neurons and ventral nerve cord projections, we used a Zeiss Axio Imager.M2 microscope with a ×40 objective. Whole-body worm images were acquired using a Leica THUNDER Imager microscope with Tile Scan function and a ×40 objective.
Human cell lines
HEK293T/17 cells (American Type Culture Collection (ATCC), CRL-11268) were plated on 0.1% gelatin-coated plates and grown in DMEM (Thermo Fisher Scientific, 11966025), supplemented with 1% MEM non-essential amino acids (Thermo Fisher Scientific, 11140035), 1% GlutaMAX (Life Technologies, 35050038) and 10% FBS (Thermo Fisher Scientific, 10500064) at 37 °C with 5% CO2. ALS-iPSCs (FUSP525L/P525L) were kindly provided by Irene Bozzoni and Alessandro Rosa37. iPSCs were cultured on Geltrex (Thermo Fisher Scientific, A1413302) using mTeSR1 medium (STEMCELL Technologies, 85850) at 37 °C with 5% CO2. All cell lines were routinely tested for mycoplasma contamination, and no contamination was detected.
Motor neuron differentiation
Motor neurons were derived from ALS-iPSCs using a monolayer-based differentiation protocol80. ALS-iPSCs were seeded on Geltrex-coated plates and maintained in mTeSR1 medium until confluent. Differentiation was initiated using neuron differentiation medium composed of DMEM/F12 and Neurobasal (1:1; Thermo Fisher Scientific, 11330057 and 21103049), supplemented with non-essential amino acids, GlutaMAX (Thermo Fisher Scientific, 35050038), B27 (Thermo Fisher Scientific, 12587010) and N2 (Thermo Fisher Scientific, 17502048).
From day 0 to day 6, the medium was further supplemented with 1 μM retinoic acid (Sigma-Aldrich, R2625), 1 μM smoothened agonist (SAG; Sigma-Aldrich, 566661), 0.1 μM LDN-193189 (Miltenyi Biotec, 130-103-925) and 10 μM SB-431542 (Miltenyi Biotec, 130-105-336). From day 7 to day 14, the neuron differentiation media were supplemented with 1 μM retinoic acid, 1 μM SAG, 4 μM SU-5402 (Sigma-Aldrich, SML0443) and 5 μM DAPT (Sigma-Aldrich, D5942). After day 14, differentiated motor neurons were dissociated and replated on poly-l-ornithine (Sigma-Aldrich, P3655) and laminin-coated (Thermo Fisher Scientific, 23017015) plates in Neurobasal medium, supplemented with non-essential amino acids, GlutaMAX, N2, B27 and neurotrophic factors (10 ng ml−1 BDNF (BIOZOL, 450-02) and 10 ng ml−1 GDNF (BIOZOL, 450-10)).
Lentiviral infection of human cells
Lentivirus (LV)-non-targeting short hairpin RNA (shRNA), LV-EPS8 shRNA 1 (TRCN0000061544), LV-EPS8 shRNA 2 (TRCN0000061545), LV-USP4 shRNA 1 (TRCN0000004039) and LV-USP4 shRNA 2 (TRCN0000004040) in the pLKO.1-puro backbone were obtained from Mission shRNA (Sigma-Aldrich). Supplementary Table 2 contains the target sequences of each shRNA construct.
To generate stable shRNA-expressing HEK293 cell lines, cells were transduced with 5 µl of concentrated lentivirus and selected with 2 µg ml−1 puromycin (Thermo Fisher Scientific, A1113803). For lentiviral infection of iPSCs, cells were dissociated using Accutase (Thermo Fisher Scientific, A1110501), and 100,000 cells were seeded on Geltrex-coated plates in mTeSR1 medium supplemented with 10 μM ROCK inhibitor for 1 day. The next day, cells were infected with 5 µl of concentrated lentivirus. Medium was replaced the following day to remove residual virus. Selection for lentiviral integration was performed using 2 µg ml−1 puromycin for 2 days.
Transfection of HEK293 cells
HEK293 cells (ATCC, CRL-11268) were seeded on 0.1% gelatin-coated plates. When cells reached approximately 40% confluency, they were transfected with 1 μg of one of the following plasmids using FuGENE HD (Promega), according to the manufacturer’s instructions: pARIS-mCherry-httQ23-GFP, pARIS-mCherry-httQ100-GFP, pLVX-Puro-TDP-43-WT, pLVX-Puro-TDP-43-A382T, pcDNA3.1-FUS-HA-WT or pcDNA3.1-FUS-HA-P525L. In the indicated experiments, cells were co-transfected with an additional 1 μg of the pCMV3-EPS8-HA plasmid. The cells were collected after 72 hours of incubation in standard medium. The pARIS-mCherry-httQ23-GFP and pARIS-mCherry-httQ100-GFP plasmids were generously provided by Frédéric Saudou81. The FUS-HA-WT and FUS-HA-P525L plasmids were a gift from Dorothee Dormann82. The pLVX-Puro-TDP-43-WT and pLVX-Puro-TDP-43-A382T plasmids were provided by Shawn Ferguson (Addgene, 133753 and 133756)83. The pCMV3-EPS8-HA plasmid was obtained from Sino Biological (HG11153-CY).
Filter trap and western blot
For filter trap assays, synchronized adult C. elegans were collected and washed with M9 buffer, and worm pellets were snap frozen in liquid nitrogen. Frozen pellets were thawed on ice and lysed in non-denaturing buffer (50 mM HEPES (pH 7.4), 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 2 mM sodium orthovanadate, 1 mM PMSF, protease inhibitor cocktail (Roche)) using a Precellys 24 homogenizer. Lysates were cleared of worm debris by centrifugation (8,000g, 5 minutes, 4 °C), and protein concentrations were determined using the BCA assay (Thermo Fisher Scientific). To assess protein levels by western blot, 30 μg of total protein was separated by SDS-PAGE and transferred to polyvinylidene difluoride membranes (Millipore). To assess aggregated proteins by filter trap, 100 μg of total protein was supplemented with SDS to a final concentration of 0.5% and loaded onto a cellulose acetate membrane assembled in a slot-blot apparatus (Bio-Rad). Then, the membrane was washed with 0.2% SDS, and SDS-resistant aggregates were detected by immunoblotting.
If lysates were used solely for western blot, worms were lysed with a Precellys 24 homogenizer in buffer containing 50 mM Tris-HCl (pH 7.8), 150 mM NaCl, 1% Triton X-100, 0.25% sodium deoxycholate, 1 mM EDTA, 25 mM N-ethylmaleimide, 2 mM sodium orthovanadate, 1 mM PMSF and protease inhibitor cocktail. Lysates were cleared at 10,600g for 10 minutes at 4 °C, and 30 μg of protein was used for western blot experiments. For analysis of polyQ monomers and SDS-insoluble polyQ aggregates, age-synchronized worms were lysed by sonication in native buffer (50 mM Tris (pH 8), 150 mM NaCl, 5 mM EDTA, 1 mM PMSF, protease inhibitor cocktail). Then, 30 μg of total protein was mixed with SDS to a final concentration of 0.4% and resolved by 12.5% SDS-PAGE.
For both filter trap and western blot analyses of C. elegans, immunoblotting was performed with antibodies against GFP (AMSBIO, TP401, dilution 1:5,000), FUS (Abcam, ab154141, clone CL0190, 1:1,000) and TDP-43 (Abcam, ab225710, 1:1,000). Additionally, for western blot experiments, immunoblotting was conducted with anti-EPS8L2 (Abcam, ab85960, 1:1,000), anti-LGG-1 (ref. 84, 1:2,000) and α-tubulin (Sigma-Aldrich, T6199, 1:5,000).
For filter trap and western blot analysis of HEK293 cell lines, the cells were collected in lysis buffer (50 mM HEPES (pH 7.4), 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 2 mM sodium orthovanadate, 1 mM PMSF, protease inhibitor cocktail), followed by homogenization through a 27-gauge syringe needle. Lysates from cells expressing pARIS-mCherry-httQ23-GFP, pARIS-mCherry-httQ100-GFP or without any overexpression were centrifuged at 8,000g for 5 minutes at 4 °C. Lysates from cells expressing FUS-HA-WT, FUS-HA-P525L, pLVX-Puro-TDP-43-WT or pLVX-Puro-TDP-43-A382T were centrifuged at 1,000g for 5 minutes at 4 °C. The supernatants were collected, and protein concentrations were measured with the BCA assay. For western blot, 30 μg of protein was analyzed as above. For filter trap analysis, 100 μg of total protein was supplemented with SDS to a final concentration of 0.5% and loaded onto a cellulose acetate membrane assembled in a slot-blot apparatus as described above. The membrane was then washed with 0.2% SDS, and SDS-resistant protein aggregates were evaluated by immunoblotting. For filter trap analysis, immunoblotting was conducted with antibodies against GFP (AMSBIO, TP401, 1:5,000), FUS (Abcam, ab154141, clone CL0190, 1:1,000) and TDP-43 (Abcam, ab225710, 1:1,000). For western blot, immunoblotting was conducted with anti-EPS8 (Proteintech, 12455-1-AP, 1:1,000), anti-β-actin (Abcam, 8226, 1:5,000), anti-HTT (Cell Signaling Technology, 5656, 1:1,000), FUS (Abcam, ab154141, clone CL0190, 1:1,000), TDP-43 (Abcam, ab225710, 1:1,000), anti-LC3B (Cell Signaling Technology, 2775, 1:1,000) and anti-USP-4 (Abcam, ab181105, 1:1,000).
For necroptosis analysis, iPSC-derived motor neurons were lysed in RIPA buffer (50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 1 mM EDTA, 1 mM PMSF, protease inhibitor cocktail). Immunoblotting was performed using anti-phospho-RIP (Ser166) (Cell Signaling Technology, 65746, clone D1L3S, 1:1,000) and anti-RIP (Cell Signaling Technology, 3493, clone D94C12, 1:1,000). Densitometry of filter trap and western blot assays was performed using ImageJ software (version 1.51).
Protein immunoprecipitation for interaction analysis
HEK293 cells were collected and lysed in a protein lysis buffer containing 50 mM Tris-HCl (pH 6.7) 150 mM NaCl, 1% NP40, 0.25% sodium deoxycholate, 1 mM EDTA, 1 mM PMSF, 1 mM sodium orthovanadate, 1 mM NaF and protease inhibitor cocktail. Lysates were homogenized through a 27-gauge syringe needle and centrifuged at 13,000g for 15 minutes at 4 °C. Supernatants were incubated on ice for 1 hour with anti-USP-4 antibody (Abcam, ab181105, 1:100). As a negative control, the same amount of protein was incubated with anti-normal rabbit IgG (Cell Signaling Technology, 2729, 1:378). Samples were then incubated with 50 µl of µMACS MicroBeads for 1 hour at 4 °C with overhead shaking. Then, the samples were loaded onto pre-cleared µMACS columns (Miltenyi Biotec, 130-042-701). The beads were washed three times with a buffer containing 50 mM Tris (pH 7.5), 150 mM NaCl, 5% glycerol and 0.05% Triton, followed by five washes with 50 mM Tris (pH 7.5) and 150 mM NaCl. The samples were eluted with 75 μl of boiled 2× Laemmli buffer, boiled for 5 minutes at 95 °C and analyzed by western blotting.
Native gels analysis
HEK293 cells expressing CMV:mRFP-Q74 (ref. 30) were lysed in buffer containing 50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 0.5% NP-40, 2 mM EDTA, 1 mM EGTA, 10% glycerol, 2 mM sodium orthovanadate, 1 mM PMSF and protease inhibitor cocktail. Lysates were homogenized using a 27-gauge syringe needle and centrifuged at 12,000g for 15 minutes at 4 °C. Supernatants were collected, and protein concentrations were determined using the BCA protein assay (Thermo Fisher Scientific). Equal amounts of protein lysates were mixed 1:1 with sample buffer (50 mM Tris-HCl (pH 6.8), 10% glycerol, 0.01% bromophenol blue). Then, 20 μg of total protein was separated using 4–15% Tris-Glycine eXtended protein gels (Bio-Rad) and imaged via fluorescence using LICOR Odyssey M.
Immunocytochemistry
Cells were fixed with 4% paraformaldehyde in PBS for 20 minutes, followed by permeabilization with 0.2% Triton X-100 in PBS (10 minutes) and blocking with 3% BSA in 0.2% Triton X-100 in PBS (10 minutes). The cells were then incubated with anti-MAP2 (Sigma-Aldrich, M1406, 1:300) and rabbit anti-cleaved caspase-3 (Cell Signalling Technology, 9661S, 1:300) for 2 hours at room temperature. After washing with PBS, cells were incubated with secondary antibodies (Alexa Fluor 488 goat anti-mouse (Thermo Fisher Scientific, A-11029, 1:500) and Alexa Fluor 568 F(ab′)2 fragment of goat anti-rabbit IgG (H + L) (Thermo Fisher Scientific, A-21069, 1:500)) and Hoechst 33342 (Life Technologies, 1656104) for 1 hour at room temperature. Finally, the coverslips were rinsed in PBS, followed by a distilled water wash, and then mounted onto microscope slides with FluorSave Reagent (Merck, 345789).
CytoD, RAC activator and DUB inhibitor treatment
For CytoD treatment, worms were collected and randomly divided equally into M9 solutions containing either 10 μM CytoD (STEMCELL Technologies, 100-0557) or DMSO as a vehicle control. The worms were incubated with CytoD or DMSO for 6 hours on a shaker. For DUB inhibitor experiments, worms were collected and randomly transferred onto plates with OP50 bacteria covered with a final concentration of 13.7 μg ml−1 PR-619 (Merck, 662141) or vehicle control (DMSO) for either 4 hours or 1 day as indicated in the corresponding figures.
HEK293 cells were treated with 2 μM CytoD or DMSO for 4 hours before lysis. For RAC activation, cells were treated with 2 U ml−1 Rac/Cdc42 Activator II (Cytoskeleton, CN02-A) for 6 hours.
Proteasome activity
Day 5 adult worms and HEK293 cells were lysed in proteasome activity assay buffer (50 mM Tris-HCl (pH 7.5), 10% glycerol, 5 mM MgCl2, 0.5 mM EDTA, 2 mM ATP, 1 mM DTT) using a Precellys 24 or a 27-gauge syringe, respectively. The samples were centrifuged at 10,000g for 10 minutes at 4 °C, and the supernatants were collected. Protein concentrations were determined using the BCA protein assay kit.
To measure chymotrypsin-like proteasome activity, 25 μg of total protein was incubated with the fluorogenic substrate Suc-Leu-Leu-Val-Tyr-AMC (Enzo Life Sciences, BML-P802) in 96-well plates (BD Falcon). Fluorescence was measured every 5 minutes for 2 hours at 20 °C (C. elegans) or 37 °C (human cells) using a microplate fluorometer (PerkinElmer, EnSpire) at 380-nm excitation and 460-nm emission.
Statistics and reproducibility
For quantification of filter trap and western blot data, we presented the results as relative changes compared with the corresponding control conditions. To average and statistically analyze independent experiments for these assays, we normalized test conditions to their corresponding control groups measured concurrently in each replicate experiment. Given that all the control groups were set to 100, we used a non-parametric Wilcoxon test when comparing two conditions to assess changes in protein aggregation and protein levels. For all other assays, we used parametric tests. Data distribution was assumed to be normal, but this was not formally tested. When more than two conditions or two independent variables were compared, we used one-way or two-way ANOVA followed by multiple comparisons tests. All statistical analyses were performed using GraphPad Prism (version 10.4.1).
For lifespan experiments, we used GraphPad Prism (version 10.4.1) and OASIS (version 1)85 to determine median and mean lifespan, respectively. The P values were calculated using the log-rank (Mantel–Cox) method and refer to comparisons between experimental and control animals within a single lifespan experiment. Each lifespan graph represents a single, representative experiment. Supplementary Table 1 contains the number of total/censored worms as well as detailed statistical analyses for each replicate lifespan experiment.
No statistical methods were used to predetermine sample size, but our sample sizes are similar to, or greater than, those reported in previous publications using the same procedures9,14,16,26,30,33,44,46,50,73,75,76,78,86,87,88. For motility assays, worms were excluded from analysis if they showed fewer than 0.1 body bends per second or were not recognized by the ImageJ software. No animals or data points were excluded from other analyses. For lifespan assays, worms were randomly picked and transferred from the synchronized population to the different experimental conditions. For all other experiments, worms were randomly distributed into the various experimental groups from single pulls of synchronized populations. Human cells were distributed to the various groups of all experiments from single pulls. Data collection was not randomized. Data collection and analysis were not performed blinded to the conditions of the experiments.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Johannesburg: South Africa have opted for experience and continuity by naming a squad of tried and tested players for the Pakistan tour and the all important 2025 Women’s ODI World Cup.
Laura Wolvaardt will lead a 15-player South Africa squad which includes the seasoned quartet of Chloe Tryon, Marizanne Kapp, Ayabonga Khaka, and Sune Luus. They will be joined by explosive batter Tazmin Brits and a group of all-rounders – Nadine de Klerk, Anneke Bosch, Annerie Dercksen, and Nondumiso Shangase.
In the wicketkeeping department, Sinalo Jafta will be partnered by 17-year-old Karabo Meso, who is set to make her senior World Cup debut. The bowling attack will be spearheaded by left-arm spinner Nonkululeko Mlaba, with seam options provided by Masabata Klaas and Tumi Sekhukhune.
Young all-rounder Miane Smit has been named as the traveling reserve, a role she also held during last year’s ICC Women’s T20 World Cup, where South Africa finished as runners-up.
“What a journey it has been. From the moment I joined this team, and even before my time when the squad went through the qualification phase, it was all about working towards this moment. We can look back at the amount of preparation we have put in and know that we have done our best. We are ready to send a squad to the World Cup that will make South Africa proud.”
“I am happy for all the players and management who will be getting on that plane to India, but I am even more excited to see what they can achieve together as a group. We believe we have the squad of players that can go out there and deliver on the world stage. Now it is all about carrying that belief with us every step of the way, along with the support of the entire nation,” said head coach Mandla Mashimbyi in a statement.
South Africa will begin their World Cup campaign against England on October 3, before facing New Zealand, India, Bangladesh, Sri Lanka, and Pakistan. They will conclude the league phase with a match against Australia on October 25.
Before the tournament, set to happen from September 30 to November 2, South Africa will travel to Lahore for a three-match ODI series against Pakistan, with matches scheduled for September 16, 19, and 22 respectively.
“The make-up of the squad is underpinned by the consistent selection process that was adhered to during the recent ICC Women’s Championship cycle, while taking into account the subcontinent conditions and the different characteristics of the group required for a successful tournament of this nature.
“A massive congratulations to all the players who have been selected for this prestigious tournament. Their hard work and sacrifice over the past three years have earned them an opportunity to represent South Africa at a 50-over World Cup – an honour a player receives a few times in their career,” said Clinton du Preez, CSA Convenor of Selectors.
South Africa squad: Laura Wolvaardt (Captain), Anneke Bosch, Tazmin Brits, Nadine de Klerk, Annerie Dercksen, Sinalo Jafta, Marizanne Kapp, Ayabonga Khaka, Masabata Klaas, Suné Luus, Karabo Meso, Nonkululeko Mlaba, Tumi Sekhukhune, Nondumiso Shangase and Chloé Tryon (DP World Lions). Travelling Reserve: Miané Smit
Healthcare-associated infections (HAIs) encompass a wide range of infections that patients may acquire during the course of treatment for other conditions within a healthcare setting, often due to factors such as invasive procedures, compromised immunity, prolonged hospital stays, or the use of medical devices like central lines, ventilators, and urinary catheters.1 Central line-associated bloodstream infection (CLABSI), ventilator-associated pneumonia (VAP), and catheter-associated urinary tract infection (CAUTI) are a few examples of HAIs that cause a significant burden on patient health and healthcare systems. In the United States (US) alone, 1 in 31 patients develop HAIs daily, leading to an annual death toll of 72,000. In addition, Meta-analyses have estimated the annual cost of HAIs in the US to be almost $10 billion.2
Among the HAIs, CLABSI stands out for its significant implications for morbidity, mortality and economic impact. It is associated with mortality rates ranging from 12% to 25% and an estimated cost of approximately $45,000 per case, largely driven by prolonged hospitalization and intensive medical care 3. Recently, the COVID19 pandemic exacerbated the situation, causing a 47% increase in CLABSI standardized infection ratios (SIRs) in the US between 2019 and 2020.3 This increase was attributed to factors such as decreased frequency of patient contact, longer durations of hospitalization, and staffing changes. While central lines are commonly used for administering certain medications, fluids, and obtaining blood samples, particularly in intensive care settings or for patients requiring long-term intravenous access,4 they are not universally required for all such interventions. Therefore, it is crucial to implement stringent infection control practices in the care and maintenance of central lines (eg, maximal sterile barrier precautions during insertion5 and standardized care bundles6) to minimize the risk of associated infections.
Traditional initiatives like the CLABSI Prevention Registered Nurse (PRN) program at a private teaching hospital in Denver, Colorado, US, have shown promise in addressing CLABSI challenges by emphasizing education, standardized care, and patient-centered outcomes.3 The program provided 24 hours of focused training on central line care and maintenance to create a specialized nurse role, significantly reducing CLABSI rates. Despite its success, the high costs and time required for training, and the risk of system bottlenecks or reliance on a few specialized individuals, could potentially create single points of failure in the infection control process.
Despite the serious consequences of CLABSIs and the considerable efforts to reduce their occurrence, including financial penalties by the Centers for Medicare and Medicaid Services, an estimated 30,100 CLABSI cases still occur annually in the US.7 This ongoing challenge highlights the urgent need for innovative, patient-centered solutions, with Artificial Intelligence (AI) offering a promising approach to address the issue. Integrating multidisciplinary frameworks and advanced risk assessment tools has shown significant potential in improving safety in critical care settings.8
The role of AI in healthcare has grown rapidly in recent years with the availability of large-scale multimodal data and advancements in computational models and algorithms.9 AI techniques offer promising potential in managing complex HAIs, such as CLABSI. By leveraging AI, healthcare providers (HCPs) can potentially identify high-risk patients, optimize prevention strategies, and enhance monitoring and response to CLABSI occurrences.
This paper explores the role of AI in managing CLABSI, with a focus on its potential to enhance patient safety and healthcare quality. It begins with a review of recent literature on AI applications in the prediction, detection, and prevention of CLABSI. Building on this foundation, the paper introduces a systems-based methodology for designing an AI-driven decision support framework that emphasizes the integration of people, systems, design principles, and risk management strategies. A comprehensive framework is then proposed for implementing AI across the CLABSI care continuum, addressing key deployment challenges and identifying opportunities for future research. The paper concludes by synthesizing the contributions of the framework and underscoring the importance of adaptable, ethically grounded AI solutions to improve outcomes in intensive care settings.
Literature Review
An earlier study explored the role of supervised machine learning and deep learning approaches in predicting CLABSI and mortality rates in patients admitted to intensive care units (ICU).7 The authors employed the common Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) III database that contains the medical records of more than 46,500 admitted cases in the US. Comparisons among algorithms, including logistic regression, gradient boosted trees, and deep learning, highlight the efficacy of deep learning in predicting mortality and central line placement, while logistic regression emerges as the most effective for predicting CLABSI among ICU patients. The findings reveal a mortality rate of 10.1% among ICU patients, with 38.4% receiving central line placement. Deep learning classifiers exhibited superior performance, achieving high area under the curve (AUC) scores for mortality prediction (0.885) and central line placement (0.816). These findings hold significant implications for decision-makers by showing that AI-based tools can enhance prediction accuracy while improving the quality of services and reducing operational costs.
Pai et al gathered data from a cohort of 5199 patients admitted to the ICU, among whom 1647 individuals developed CLABSI, while the remainder developed non-bloodstream infections.10 The data was collected at Taichung Veterans General Hospital from 2015 to 2019. The study employed five different machine-learning models to predict CLABSI cases. The findings suggested that alkaline phosphatase (ALKP) and central venous catheter (CVC) period as key predictors and indicators for bloodstream infections. In addition, the findings highlighted that the random forests model produced the highest prediction accuracy with an area under the receiver operating characteristic (AUROC) curve of 0.855 and 0.851 for the validation and testing datasets, respectively. Moreover, the study provided information on the appropriate cut-off laboratory values for bloodstream infection diagnostics and how varying these cut-off thresholds would affect the model accuracy. The authors argued that leveraging AI tools to manage CLABSI would result in better predictions, enabling timely and more efficient treatments.
In a recent article, researchers sought to predict impending CLABSIs in hospitalized cardiac patients.11 Using a machine-learning model, specifically a random forest classification, researchers aimed to predict which patients admitted to the cardiac ICU or cardiac ward at Boston Children’s Hospital would develop a CLABSI within 24 hours of admission. Data collection spanning January 2010 to August 2020 included variables related to infection occurrence from patients with CVCs admitted to specified units, excluding those with bacterial endocarditis. The study encompassed 104,035 patient-days and 139,662 line-days from 7468 unique patients, with 399 positive blood cultures, predominantly Staphylococcus aureus as the pathogen. Key predictors of CLABSI included prior infection history, elevated heart rate and temperature, increased C-reactive protein levels, parenteral nutrition exposure, and alteplase use for CVC clearance. The predictive model successfully identified 25% of positive cultures with a false-positive rate (FPR) of 0.11% and AUC of 0.82. This study represents an initial step toward developing a CLABSI alert system to enhance current practices, potentially improving patient outcomes and contributing to cost savings.
A recent systematic review aimed to thoroughly evaluate evidence-based interventions designed to prevent and reduce the incidence of CLABSIs in adult intensive care settings.12 The review concentrates on attaining a zero-incidence rate of CLABSIs, with a focus on applying positive displacement needleless connectors. The scope of the analysis includes research published from January 2016 through June 2020, with a specific emphasis on adult ICU environments. The review’s findings highlight the practicality of achieving a zero-incidence rate of CLABSIs through the strategic implementation of positive displacement needleless connectors, with a suite of supplementary interventions. These additional measures encompass the deployment of checklists and vigilant monitoring of the central line care bundle, the introduction of silver-impregnated dressings, the continuous education of ICU staff, bedside monitoring in real-time, and the compulsory reporting of CLABSI occurrences. While the systematic review did not focus on AI interventions, its findings hold crucial implications for nursing practice and policy. They emphasize the importance of strict adherence to infection control standards and evidence-based practices to lower CLABSI rates, ultimately reducing healthcare expenditures. Moreover, the review stresses the critical need to integrate CLABSI prevention protocols into nursing curricula to bolster the knowledge and clinical expertise in the domain of infection prevention and control.
Further, researchers, in another study, developed a machine learning algorithm (MLA) aimed at predicting the likelihood of CLABSI development before central line placement during a patient’s hospital stay.13 This MLA utilizes electronic health record (EHR) data, minimizing disruption to clinical workflows. The study employed three supervised machine learning classifiers: XGBoost (XGB), logistic regression, and decision tree models. These classifiers retrospectively analyzed EHR data from 27,619 patient encounters. XGBoost emerged as the top performer, achieving an AUROC curve of 0.762 for CLABSI risk prediction 48 hours post-central line placement. By identifying at-risk patients, improving monitoring, modifying treatments, and reducing infection rates, this approach ultimately leads to enhanced patient outcomes and cost savings. These models offer early indicators of patient susceptibility to CLABSI post-central line placement, aiding clinical decision-making through risk-based patient stratification. This process addresses the lack of tools for CLABSI risk stratification and facilitates proactive management and prevention of CLABSIs in clinical settings. The gaps and limitations of the study include further validation in live clinical settings and the tuning of machine learning algorithm parameters to individual hospitals.
Moreover, Beeler et al emphasized the pivotal role of real-time monitoring and prediction in reducing hospital stay time and costs associated with CLABSI.14 Therefore, the authors utilized the data from three tertiary hospitals in the US to develop various random forest models to predict CLABSI. The best-performing model was found to produce an AUROC curve of 0.82. These models support decision-makers in efficiently allocating resources for CLABSI prevention by identifying high-risk patients who would benefit from timely interventions most. This approach can enhance patient care quality and mitigate healthcare costs.
Another previous investigation presented a comprehensive exploration of the application of AI in HAI surveillance.15 The study aims to enhance surveillance, improve laboratory diagnosis, and educate on hand hygiene within the realm of infection prevention and control (IPC). Through the evaluation of AI tools such as OpenAI’s ChatGPT Plus (GPT-4) and the Mixtral 8×7b-based local model, the research shows the potential of AI in accurately identifying HAIs, particularly CLABSI and CAUTI. Findings reveal that while AI demonstrates proficiency in detecting HAIs with clear prompts, challenges arise with ambiguous inputs, highlighting the necessity of clear communication and human oversight. Furthermore, the study elucidates AI’s role in epidemiology, laboratory diagnosis, and hand hygiene education, emphasizing the need for prospective evaluation in real-world clinical settings and close collaboration with IPC experts to ensure clinical relevance. The implications of AI in HAI surveillance extend to healthcare efficiency, quality improvement, resource allocation, and educational value, suggesting its potential to significantly enhance healthcare outcomes and operational efficiency when integrated judiciously into IPC measures.
A prior study conducted between 2015 and 2017 in two adult tertiary care hospitals in the US aimed to implement and sustain evidence-based behaviors and practices to reduce annual CLABSI.16 Employing an agile implementation model, the authors systematically identified areas for potential enhancement and thoroughly reviewed the literature to identify the most effective evidence-based practices in mitigating the problem. Moreover, the model utilizes AI tools to identify non-value-added activities and problems that result in wasting resources. This methodological approach highlights the healthcare system within the hospital as a Complex Adaptive System (CAS), facilitating a better understanding of the hospital’s capacity to adjust to dynamic environments. The study reported a significant reduction in CLABSI rate from 1.76 to 1.24 per 1000 days, resulting in higher quality and more efficient healthcare services. Table 1 summarizes the range of challenges and AI solutions aimed at addressing CLABSI, as outlined in the reviewed literature.
Table 1 Comparative Analysis of AI Solutions for Predicting and Preventing CLABSI
The table showcases a diversity of predictive models and systematic interventions employed to mitigate the risks and impacts of CLABSIs. Notable among these are the use of deep learning, logistic regression, and random forests, which have demonstrated high predictive accuracies for CLABSI occurrences and patient outcomes in ICUs. These models leverage large-scale databases and patient records to enhance prediction and treatment protocols. Systematic interventions highlighted in the table include the implementation of the AI model employing the CAS framework to identify non-value-added activities and tailor evidence-based solutions to the unique environment of healthcare settings. However, the limitations outlined indicate potential biases from retrospective data, reliance on ICD coding, which may lack sensitivity, and challenges in generalizing findings across different healthcare settings. Ultimately, these studies showcase the potential of integrating AI and machine learning into healthcare practices to improve predictive accuracy, optimize resource allocation, and enhance patient outcomes in the fight against CLABSIs.
Methodology: A Systems Approach
The design and development of an AI-driven decision support framework for CLABSI management necessitates a comprehensive and systematic approach to ensure effectiveness, safety, and adherence to ethical standards. The systems approach offers a robust methodology for addressing complex problems such as CLABSI by considering the entire ecosystem in which these problems exist.17 This approach is characterized by four key components: people, systems, design, and risk, as visualized in Figure 1.18 Each component plays a vital role in the framework’s development and operationalization:
People: Identifies who will use the AI system within CLABSI management, including HCPs, patients, and administrative personnel. It locates the system within various healthcare environments where it will be deployed and situates it within the complex dynamics of healthcare interactions.
Systems: Understands the roles of different stakeholders interacting with the system. It organizes the technological and organizational infrastructures, including data management systems and existing healthcare IT infrastructure, and integrates these components to ensure seamless interaction between the systems and the AI tools.
Design: Explores the specific needs of the end-users of the AI system in the context of CLABSI management. It creates not only the technical aspects of the system, such as algorithms and models, but also the user interface and experience design, ensuring usability, accessibility, and efficacy.
Risk: Examines the current procedures in CLABSI management, assesses the potential risk factors, and proposes improvements to mitigate these risks.
Figure 1 Conceptual framework of the systems approach.
Adopting a systems approach ensures a holistic consideration of the complex interdependencies within healthcare settings.19 It aids in the creation of a decision support framework that is not only technologically advanced but also socially acceptable and institutionally integrable. This methodology section will delve deeper into each of these components, detailing how they contribute to the development of a comprehensive, effective, and ethically sound AI-driven framework for managing CLABSI.
People
In addressing the people aspect of the systems approach, it is crucial to identify the key stakeholders who will interact with and utilize the AI system within CLABSI management. These stakeholders include patients, HCPs such as physicians, nurses, administrative personnel, and infection control specialists, who are responsible for diagnosing, treating, and preventing CLABSI cases. HCPs rely on AI tools to enhance decision-making processes, optimize treatment plans, and improve patient outcomes. Patients, on the other hand, as the recipients of care, may benefit from AI-driven interventions that facilitate early detection and prevention of CLABSI. Their engagement in the prevention and management process is essential for the successful implementation of AI-driven solutions. Additionally, administrative personnel, such as hospital administrators and information technology staff, are responsible for overseeing the integration of AI technologies into existing healthcare systems, ensuring smooth operation, and monitoring outcomes.
The AI system is situated within various healthcare environments, such as hospitals, and long-term care facilities, where CLABSI management is a critical concern. As the system is deployed in these settings, it is essential to consider the complex dynamics of healthcare interactions, including interdisciplinary collaboration, communication, and decision-making processes. Understanding the roles and responsibilities of each stakeholder group, as well as the barriers and facilitators affecting their ability to use the AI system, is crucial for its successful implementation. Further, factors such as organizational culture, workflow processes, resource availability, and stakeholder engagement influence how the AI system is utilized and integrated into the overall healthcare framework. By addressing the questions of who will use the system, where the system will be deployed, and the factors affecting the system, we can ensure that the AI-driven solution for CLABSI management is tailored to the unique needs and challenges of the healthcare sector, ultimately improving patient outcomes and reducing the burden of CLABSI on healthcare systems.
Systems
This part of the systems approach deals with organizing and integrating various entities, including people, Information Technology (IT), and data warehouses, to ensure seamless interaction among them. In this context, one major step is considering the stakeholders of the proposed system and understanding their needs, notions, and requirements.20 In our system, the main stakeholders are the patients, HCPs, staff, and management. Moving to other elements of the system that need to be addressed and understood and to highlight the importance of identifying the elements of the decision-making process, a study was conducted to review system elements affecting the disposition decision-making in the emergency room.21
In the proposed model, different elements need to be considered collectively in a holistic view. For instance, budgetary constraints, the procurement of medical equipment and devices, training and workshops for the HCPs are all elements that should be considered in the system. Finally, in this step, a deep understanding of the interactions between the previously discussed stakeholders and elements must occur. The integrated system description must abide by the output of previous steps by utilizing the components and elements of the system to achieve the intended outcomes. The representation of the interactions among the system’s elements is crucial for understanding system behavior, dependencies, and relationships. For instance, the hospital management will be concerned with different medical equipment, data, and budgetary constraints. Also, resistance to change will be faced by patients, staff, and HCPs. Training and culture adoption will have to be presented by physicians and care providers. The mechanism of how the system performs should be communicated in various ways, including graphical representation. Flowcharts, Unified Language Modeling (UFL), Entity-Relationships Diagrams (ERD), and Network Diagrams are effective tools for communicating how systems work. The role of graphical representation is essential in discovering and communicating value by understanding the current state, and stakeholders’ prospects.22
Design
The third section of the system thinking framework is design. This section mainly explores the needs and requirements for framework development and investigates how these needs are met and how well they are met. The examined literature expressed the urgent need for a more efficient and patient-centric approach to managing CLABSI.9 This will reduce the risk associated with confirmed CLABSI cases. In addition, Scardoni et al emphasized that it is pivotal for HCPs to develop a more cost-effective approach for dealing with and managing CLABSI.2 Accomplishing that allows for better understanding of CLABSI and the complexities associated with preventing and managing it. To accomplish these needs, there is a need for a better understanding of the role of AI in managing CLABSI, developing tailored AI algorithms for predicting and detecting CLABSI and integrating AI with clinical expertise to make better data-driven decisions. Lastly, assessing how well the needs are met is a crucial part of the design phase in the system thinking approach. Therefore, there is a need to develop specific, measurable metrics and criteria such as the time needed to identify a CLABSI case, the percentage of reduction in CLABSI cases, the mortality rate associated with CLABSI, the cost of managing a CLABSI case from detection to full recovery. Additionally, the implemented AI models and algorithms must be refined and updated regularly to boost their accuracy and performance.
Risk
CLABSI management encompasses prevention, detection, and resolution, involving rigorous infection control protocols, such as strict hand hygiene, barrier precautions during catheter insertion, and continuous surveillance. Integrating AI into such critical healthcare processes aims to enhance efficiency and efficacy but also introduces significant challenges. These include issues related to data quality, model accuracy, and system integration that must be constantly monitored.
AI models may inaccurately predict potential CLABSI cases, which can result in bypassed prevention protocols or misdiagnoses. In critical healthcare settings like the ICU, such errors could delay necessary interventions, worsening patient conditions and possibly resulting in fatal outcomes. The lack of generalization in datasets is a glaring limitation of the discussed literature. Models trained on retrospective data from single clinics may not perform well universally due to dataset shifts, a phenomenon where models underperform post-deployment due to discrepancies between training environments and real-world application contexts.23 Furthermore, data breaches could expose sensitive patient information to unauthorized individuals or malicious actors, undermining trust, and compliance with privacy laws. Moreover, legal and ethical challenges arise when AI-driven decisions result in patient harm, complicating the determination of liability, especially with the lack of patient consent. Finally, integration risks include resistance from healthcare staff, inadequate training on new systems, and the potential for increased workload due to dual management systems.
To mitigate these risks, it is essential to validate and enhance the AI models using diverse, multi-site datasets and clinical trials to improve generalization. These models must also continually adapt to new data and clinical advances. Strengthening cybersecurity measures is critical to protect patient data, while maintaining clinical oversight ensures that AI supports, rather than replaces, professional judgment. Addressing ethical concerns involves clear guidelines for transparency and accountability. Effective integration of AI requires comprehensive training for all end-users and fostering a culture that values both technological advancements and traditional healthcare principles. By implementing these strategies, the potential of AI to enhance CLABSI management can be realized while ensuring safety, efficacy, and ethical compliance.
Proposed Framework
The proposed framework starts with explaining the role of AI in managing CLABSI and how AI can be integrated with clinical expertise to provide a more efficient and patient-centric healthcare system. This framework consists of three primary stages: prevention, detection, and management. In the prevention stage, patients are classified based on their probability of developing CLABSI. This allows HCPs to take proactive early measures to prevent infection. The detection stage focuses on patients who were not initially predicted to be at risk but later exhibit clinical indicators suggestive of CLABSI such as fever, chills, hypotension, or abnormal laboratory results (eg, elevated white blood cell count, positive blood cultures).24 These symptoms and signs are typically documented through routine clinical observations, diagnostic tests, and EHR inputs. AI tools are employed to identify CLABSI and provide the necessary information to allow physicians to make a data-informed decision. Lastly, the management stage identifies high-risk patients, allowing for better resource allocation and prioritization and developing personalized treatment plans. Figure 2 provides a clear illustration of the developed framework.
Figure 2 Proposed framework for the role of AI in managing CLABSI.
Prevention
The prevention stage of our proposed AI-driven solution framework focuses on proactive measures to mitigate the risk of CLABSI in newly admitted ICU patients who are scheduled for central line placement. Utilizing historical EHR alongside real-time ICU monitoring data, our framework employs deep learning, logistic regression, and random forest models discussed in the literature review7,11,13 to predict the likelihood of CLABSI development. These models integrate data securely stored in the hospital’s data systems, ensuring robust data protection.
The system of joint models assesses the probability of developing CLABSI within 24 hours of admission to the ICU and classifies cases as high risk when they exceed a predetermined probability threshold. When a high risk of infection is predicted, the system generates real-time alerts via the HER system or clinical communication platforms. These alerts are directed not only to attending physicians but also to the broader clinical care team, including nurse practitioners (NPs), physician assistants (PAs), and covering providers, based on the current assignment and availability of care team members. The alerts include relevant clinical data points such as elevated temperature, white blood cell count, catheter placement time, and vital sign trends. This ensures timely awareness and action, even in situations where the primary physician is off-duty or care has been transitioned to another provider, thus, maintaining a crucial human element in the decision loop to counteract potential risks associated with automation reliance and model inaccuracies in practical applications. This stage not only anticipates CLABSI occurrence but also empowers HCPs with actionable insights, significantly enhancing patient safety and care efficiency.
Detection
The detection stage of our proposed AI-driven solution framework is critical for the early identification of CLABSI as they occur. This stage begins when a patient who passes the prevention stage exhibits the symptoms, previously discussed, that could indicate an infection. Employing AI tools to analyze real-time data streams from clinical monitoring systems and laboratory results, our framework uses a combination of random forests, decision trees, and LLMs, as highlighted in our literature review.10,15 These models not only accurately predict but also provide a level of explainability, aiding HCPs in their diagnostic processes with crucial insights. Upon detecting potential signs of CLABSI, the AI system promptly alerts clinical teams (infectious disease specialists, infection prevention practitioners, or critical care physicians) to further assess the situation. This prompt detection is vital as it allows for the immediate allocation of specialized personnel to confirm the presence of CLABSI, ensuring swift intervention. Should a CLABSI case be confirmed, it signals a transition to the management stage; however, it also indicates a miss in our prevention stage, marking the incident as a false negative. This instance is subsequently fed back into the hospital’s secure database. Such cases are invaluable for retraining our prediction models, enhancing their accuracy, and adapting to dataset shifts.
Integrating human verification into this process is critical for managing accountability and reducing automation bias. Moreover, rapid AI-driven detection and preliminary diagnosis save valuable time for HCPs, making this approach cost-effective. It expedites the diagnosis process, allowing for quicker responses that can potentially reduce the duration of infection and associated healthcare costs. The detection stage not only improves patient outcomes by enabling timely interventions but also contributes to the overall cost-effectiveness of managing healthcare resources in a high-stake environment, such as ICU.
Management
The management phase starts after determining confirmed CLABSI cases. During this phase, supervised machine learning and deep learning algorithms are deployed to categorize the confirmed cases into various risk levels. This categorization helps differentiate patients based on their risk of developing CLABSI, classifying them as high-risk, medium-risk, or low risk patients. Consequently, the decision makers can strategically allocate and prioritize resources such as computational capacity, medical devices, and HCPs, leading to enhanced effectiveness, efficiency, and cost-effectiveness in patient care delivery. During continuous monitoring of confirmed CLABSI cases, real-time data is fed back to the hospital’s main data server. This significantly increases the amount of data on the hospital’s server, which can be utilized to develop more accurate and efficient models. Moreover, a major aim of the management phase is to employ interdisciplinary collaboration among AI algorithms and clinical expertise to develop personalized treatment plans for infected patients. In fact, personalized treatment plans have been proven effective in treating a wide range of diseases.25,26 Despite their effectiveness, the examined literature showed no evidence that such plans have ever been developed for CLABSI patients. Therefore, this paper fills this gap by recognizing the role of personalized treatment plans in enhancing healthcare quality and safety for CLABSI patients. In addition, they contribute to reducing the hospital’s CLABSI management costs, which in turn results in higher profits and better allocation of resources.
Deployment Challenges
The deployment of an AI-driven solution framework for the prevention, detection, and management of CLABSI in ICU environments introduces several challenges that are both technical and organizational in nature. These challenges include issues related to system integration, human oversight, stakeholder resistance, and the need for adaptability in complex clinical settings. As highlighted in the proposed framework (Figure 2), the interaction between multiple system components demands seamless integration with EHR systems and clinical monitoring tools. However, many healthcare institutions still face data silos and interoperability barriers, which may limit the effectiveness of AI models designed to predict or detect CLABSI. Addressing these limitations requires the development of standardized data pipelines and secure, real-time interfaces between clinical databases and AI modules. Future research should focus on creating robust architectures that allow these integrations to occur with minimal disruption to existing workflows.
Another major challenge is the human factor, particularly trust, acceptance, and competence in engaging with AI tools.27 While top-level management may resist implementation due to concerns about high initial investment and uncertain return on investment, frontline HCPs may express skepticism over model accuracy, fear of deskilling, or apprehension about being replaced. These concerns, if unaddressed, could result in low adherence to AI-generated alerts, especially in critical infection control scenarios such as CLABSI management. Patients, too, may hesitate to consent to treatment plans perceived as overly dependent on automated decision-making, particularly if transparency in how AI supports clinical judgment is lacking. To address these challenges, a targeted communication strategy must be implemented for each stakeholder group, outlining the system’s objectives, implementation timeline, and safeguards in place to ensure human oversight and accountability. Additionally, training programs that involve clinicians in the development and testing phases can foster a sense of ownership and build trust in the technology.
Equally important is the development of personalized treatment strategies based on AI insights. While our framework emphasizes such plans in the management stage, current literature lacks examples of their application to CLABSI specifically. Future research should explore how supervised learning models can inform individualized care pathways for infected patients, potentially improving outcomes while optimizing ICU resource allocation. Real-time data captured during ongoing CLABSI cases could be used not only for monitoring but also for continuously retraining and improving prediction models, making the system more adaptive to clinical and environmental shifts. The integration of wearable biosensors and mobile monitoring technologies represents another promising direction for enhancing early detection and patient-specific intervention.
Finally, from a financial and policy standpoint, administrative hesitation often stems from uncertainty about cost-effectiveness. A well-documented cost-benefit analysis demonstrating reductions in CLABSI-related complications, ICU stays, and antibiotic use can provide the necessary justification for investment. Exploring funding models and regulatory incentives to support AI integration in infection control efforts will be critical to broader adoption. In sum, while deploying AI in the fight against CLABSI presents a range of challenges, each of these obstacles can be mitigated through focused stakeholder engagement, targeted training, interdisciplinary collaboration, and continued research into adaptive, personalized, and scalable AI solutions. Figure 3 provides a visual representation of the interconnections among these components and highlights the importance of addressing them as a cohesive system.
Figure 3 Deployment challenges and interactions in the proposed framework.
To refine the proposed framework, future research should investigate adaptive learning models capable of updating in near-real-time as new CLABSI cases emerge. Another promising area involves integrating AI with bio-sensing wearables for earlier symptom detection. Furthermore, evaluating the impact of personalized AI-driven treatment plans, currently underexplored in CLABSI literature, should be a priority. Cross-institutional collaborations could validate model generalizability across diverse ICU settings.
Addressing these deployment challenges in a targeted and evidence-based manner is essential for realizing the full potential of AI in reducing CLABSI incidence, enhancing patient outcomes, and optimizing ICU resource allocation.
Conclusion
The development of an AI-driven decision support framework for CLABSI management requires a comprehensive and systematic approach to ensure effectiveness, safety, and ethical alignment within clinical environments. Leveraging a systems approach allows for addressing the multi-dimensional nature of CLABSI by considering the interconnected components (clinical workflows, data infrastructure, human actors, and institutional policies) that influence both infection risk and treatment outcomes. Our proposed framework incorporates four core components: People, Systems, Design, and Risk, each of which plays a crucial role in enabling the successful implementation and sustainability of the solution.
The framework is structured into three primary stages: prevention, detection, and management, each addressing distinct needs in the CLABSI care continuum. In the prevention stage, AI functions to stratify patients based on their individual risk profiles, using historical and real-time data to anticipate CLABSI onset and prompt timely clinical intervention. In the detection stage, AI analyzes dynamic clinical indicators such as vital signs, lab results, and patient symptoms to identify potential infections, providing alerts and interpretability that support swift diagnostic confirmation. During the management stage, AI assists in classifying confirmed CLABSI cases into severity levels, enabling better resource prioritization and the formulation of personalized treatment strategies, an area currently underrepresented in CLABSI care literature.
Despite its potential, deploying AI for CLABSI management faces several domain-specific challenges. These include the need for accurate and interoperable data inputs that reflect the nuances of infection patterns in ICU settings, clinician trust in AI predictions particularly when dealing with critical infections, and resistance from stakeholders wary of over-reliance on technology. Additionally, the rarity and variability of CLABSI cases create challenges in model generalizability and necessitate ongoing data collection and model retraining. Future AI systems must therefore be adaptable, transparent, and integrated within clinician workflows to be truly effective.
By applying a systems approach and addressing the clinical, technical, and organizational complexities unique to CLABSI, this framework provides a pathway toward safer, more responsive, and cost-effective care. It emphasizes the role of AI not as a replacement, but as a complement to clinical expertise, supporting early intervention, accelerating accurate diagnosis, and guiding personalized treatment decisions. Ultimately, this contributes to improved patient outcomes, reduced healthcare costs, and enhanced quality of care in intensive care environments where every second matters.
Disclosure
The authors report no conflicts of interest in this work.
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All but two Western leaders shy away from Xi’s paradepublished at 08:44 British Summer Time
08:44 BST
Paul Kirby Europe digital editor
Image source, Reuters
Image caption,
Putin shaking hands with Slovakia’s Fico in China on Wednesday
Most Western leaders have chosen not to attend China’s “Victory Day” parade.
The two who have gone, Slovakia’s Robert Fico and Aleksandar Vucic of Serbia, are no strangers to controversy but they have very different reasons for being there.
Both attended Russia’s Victory Day parade last May, and both have met Russia’s Vladimir Putin in Beijing, just as they did in Moscow.
But Vucic’s Serbia, although a candidate to join the EU, has cultivated close economic ties with China and he has sought to steer a neutral course with Russia, despite condemning its war in Ukraine.
Fico is in a very different position.
His country is a member of both the EU and Nato, and yet he has called for relations with Russia to be normalised.
While the EU wants a halt to Russian oil and gas imports by the end of 2027, Fico wants his Russian energy supply increased and Ukraine has sought to disrupt the pipeline providing it.
Putin may compliment the Slovak leader on his “independent foreign policy”, but Slovakia’s European allies don’t see it quite that way.
Image source, SPUTNIK/KREMLIN/EPA/Shutterstock
Image caption,
Serbia’s Vucic also met Putin
Asked whether Fico was representing the EU in Beijing, a European Commission spokeswoman gave a terse “no”, pointing out that China was a “key enabler” of Russia’s full-scale war in Ukraine.
His opponents at home accuse him of serving Russian propaganda and betraying his country.
Fico argues he’s in Beijing “as a new world order is being formed” and says that after talks with Putin he has come to several conclusions and has a “serious message” for Zelensky when they meet on Friday.
Melvyn Bragg has stepped down as host of BBC Radio 4’s In Our Time after 26 years.
Lord Bragg has hosted more than 1,000 episodes of the discussion programme since its launch in 1998, including its most recent series, which aired earlier this year.
The show saw him lead conversations with academics about a wide variety of historical, scientific, philosophical and cultural topics – from Alice’s Adventures in Wonderland to Zenobia, Queen of the Palmyrene Empire.
The 85-year-old thanked listeners, saying it had been “a great privilege and pleasure” to present the show. He said he would continue to work with the station, which will announce his replacement in due course.
“For a programme with a wholly misleading title which started from scratch with a six-month contract, it’s been quite a ride!” said Lord Bragg in a statement.
“I have worked with many extremely talented and helpful people inside the BBC as well as some of the greatest academics around the world.”
The BBC stressed while “he will be much missed” on In Our Time, “Melvyn will continue to be a friend of Radio 4”, teasing a new project next year.
Radio 4 controller Mohit Bakaya said Lord Bragg had “been part of the heartbeat of Radio 4 for over three decades”.
“His fierce intellect, coupled with a wonderful curiosity and extraordinary passion for knowledge, marks him out as one of the broadcasting greats,” he said.
To mark “the end of an era”, he said Radio 4 would air some of “Melvin’s most cherished episodes” later this year, when there will also be a curated selection on BBC Sounds chosen by some of the show’s most famous listeners.
In Our Time is one of BBC Sounds’ most popular podcasts among listeners aged under 35, the corporation said.
BBC director general Tim Davie said Lord Bragg’s “passion for the arts, his intellectual curiosity, and his unwavering commitment to public service broadcasting over the last 60-plus years have enriched the lives of millions”.
He added: “Through In Our Time on Radio 4 he has brought depth, insight, and humanity to our airwaves every single week for more than a quarter of a century.
“He leaves behind not just an extraordinary body of work, but a gold standard of broadcasting and interviewing excellence that will inspire generations to come.”
Lord Bragg joined the BBC in 1961 and presented Radio 4’s Start the Week for a decade before In Our Time began. He is also known for fronting arts magazine series The South Bank Show, which was broadcast on ITV and later Sky.
MS Dhoni Is a Hero Beyond Borders And Pakistan Captain’s Remark Says It All: ‘Saw Him Lead India And CSK…’
Photo : AP
MS Dhoni is arguably India’s greatest captain, having led the team to glory in the T20 World Cup, ODI World Cup, and ICC Champions Trophy. But his legacy goes far beyond the silverware as he became an enduring source of inspiration with his on-field persona and remarkable ability to remain calm under pressure – even for cricketers outside India.
Pakistan Women’s team captain Fatima Sana also draws inspiration from the former India captain, and said she learned a lot watching the talismanic captain lead teams in international cricket and IPL. Fatima is set for a huge task of leading Pakistan during the Women’s World Cup.
“It is natural to be a little nervous initially when captaining in a big tournament like the World Cup, but I take inspiration from Mahendra Singh Dhoni as a captain,” Fatima told PTI Bhasha in an interview ahead of the World Cup.
“I have seen his matches as India and CSK captain. His on-field decision-making, calmness and the way he backs his players, there is a lot to learn from that. When I got the captaincy, I thought that I have to become like Dhoni. I also watched his interviews and got to learn a lot,” she said.
Pakistan Look To Change History At Women’s World Cup
Pakistan’s women’s team has underperformed in ODI World Cups so far, but the captain is optimistic that the team has enough to turn the tables this time around.
“This time, the jinx will definitely be broken because the young players know how important this tournament is for Pakistan women’s cricket. We will not think about the past. My goal is to take the team to the semifinals,” she added.
“In Pakistan, girls have started playing cricket in schools and international matches are being telecast live. ICC has also taken a good initiative by increasing the prize money for the Women’s World Cup, which will inspire budding players in Pakistan. But there is still a barrier which we have to break through this tournament,” she said.
“In our country, women’s cricket is not seen as a career option. But if we play well, it will make a huge difference. Our effort will be to inspire parents in Pakistan to encourage their girls to make a career in sports,” said the all-rounder.
Fatima’s Big Prediction For India
Fatima said India will be under pressure to win the World Cup at home, but added that familiarity with the conditions would be a big plus for the hosts
“My favourite team is Australia. It is difficult to predict the semifinalists but India’s performance has been very good in the last few years. They have very experienced players like Jemimah (Rodrigues), Smriti (Mandhana) and Harmanpreet (Kaur) but we will not focus on any one player.” She also said that being the hosts, there will be added pressure on India but there will also be the advantage of playing on home grounds.
“India have never won the World Cup and being the host, there will be pressure to win. But along with this, the presence of home fans also boosts the morale. It depends on the team how it takes it.” A big fan of Australian star Ellyse Perry, Fatima started playing street cricket with her brothers at the age of 11 in Karachi. She lost her father during the T20 World Cup last year, but chose national duty over personal grief.
Highlights include the hyper-versatile ZIP modular projection platform and the audio-centric BOOM series; live demos at Hall H21, Booth H21-111.
BERLIN, Sept. 3, 2025 /PRNewswire/ — Aurzen, a leading innovator in smart projection technology, today announced it will showcase its comprehensive product ecosystem at IFA 2025 from September 5–9. Press and attendees are invited to Messe Berlin, Hall H21, Booth H21-111, for live demonstrations.
At the heart of Aurzen’s showcase are three distinct product families: the hyper-portable ZIP modular ecosystem, the premium audio-integrated BOOM series, and the feature-rich, budget-friendly EAZZE series. Together, they represent Aurzen’s commitment to creating a perfect projection solution for every user, from the digital nomad to the home cinema enthusiast.
The ZIP Family: A Modular Pocket Projection Platform
Aurzen’s flagship ZIP ecosystem redefines on-the-go entertainment with its unique modular design, allowing users to build their perfect portable setup. The complete ZIP ecosystem will be demonstrated for the first time at IFA 2025.
ZIP Projector: An ultra-compact 720p HD projector with Time-of-Flight autofocus and keystone correction for instant, perfect pop-up screenings anywhere.
Seamless Streaming & Gaming: The CastPlay-C Dongle provides plug-and-play, DRM-certified streaming from Netflix, Disney+, among others, and mirrors a Nintendo Switch with pass-through charging.
Project Anywhere: The innovative Suction Cup Mount attaches to any smooth surface, while the Battery Stand extends the runtime and provides stable placement for a truly mobile cinema.
Instant Screen: The ScreenPlay Portable A3 Display is a lightweight, foldable 16:9 screen that sets up in seconds.
The BOOM Series: Premium Audio Meets Powerful Projection
The BOOM series integrates high-fidelity audio directly into a high-performance projector, eliminating the need for external sound bars.
BOOM mini: Delivers a cinematic experience with native 1080p resolution, ultra-low 50ms latency for gaming, and powerful built-in 2x10W Dolby Audio speakers. Its transparent back panel puts the powerful speakers on display, offering a visual dimension to the immersive audio experience.
BOOM air: Features a searchlight-inspired design with 300 ANSI lumens, built-in Google TV, and 10W Dolby Audio. Both models include a 110° gimbal stand for easy, off-center image alignment. BOOM air features a visible speaker in the back.
The EAZZE Series: Feature-Rich Projection Without the Premium Price
Aurzen makes cutting-edge technology accessible to everyone with its EAZZE series. A highlight is the D1R Cube(available Sept. 2025), the world’s first projector with Roku OS, delivering streaming simplicity in a compact form. Other models include the powerful D1 Max (950 ANSI lumens, Google TV) and the entry-level D1G, which packs autofocus and auto-keystone into an affordable package.
D1 Max (coming Q4 2025): 950 ANSI lumens, native 1080p, 1,500:1 contrast, autofocus & keystone, karaoke-ready mode, 2GB RAM, 16GB memory, Google TV OS, and dual 8W speakers.
D1R Cube with Roku OS (available September 2025): The Aurzen Roku TV smart projector is the world’s first Roku OS projector. Unique features include an intuitive Roku remote and wireless audio expandability, allowing users to pair Roku Wireless Streambars or Roku Wireless Speakers for cinema-like sound.
D1G: Built-in Google TV, autofocus, and auto-keystone correction, all at a budget-friendly price.
The three distinct product families—the hyper-portable ZIP, the audio-centric BOOM, and the value-focused EAZZE—are engineered to deliver a specialized experience for every lifestyle and budget. This strategic approach ensures that whether a user prioritizes portability, all-in-one cinematic sound, or accessible smart features, Aurzen has a purpose-built solution.
About Aurzen
Aurzen is a global innovator in smart projection technology, dedicated to creating high-value, user-friendly entertainment solutions for modern lifestyles. Combining cutting-edge optical engineering with intuitive software and user-centric design, Aurzen delivers products that bring big-screen experiences to any space. The company pioneered the world’s first Tri-Fold Truly Portable projector, ZIP, and its designs have been recognized with multiple international awards, including iF Design Award, Red Dot Award, G-Mark Award and IDEA Award.
Industrial data fabrics also allow organizations to eradicate the complicated web of point-to-point connections and add context between the OT layer and IT layer. With an industrial data fabric acting as the unifying data and message broker, hyper connectivity can still be established between OT and IT systems across an organization’s geographic and enterprise network. It facilitates the on-premises-to-cloud data flow into a single, encrypted port rather than exposing hundreds of open ports like classical point-to-point protocols do.
By managing OT data following the fabric paradigm, industry can more easily analyze and secure it at a lower total cost of ownership. Moreover, this approach promotes industry compliance and audit trails, ensuring that sensitive data is safely protected, without silos getting in the way.
Eliminating data silos and adding context
Beyond cybersecurity, there are several benefits to eradicating data silos with a fabric-enabled data management strategy.
In years past, fragmented data management practices not only exacerbated cyber risk, but created data silos across the business. In these cases, data tended to be trapped in smart devices, SCADA systems or distributed control systems (DCS) or shared to the cloud without any context. Different departments or systems were tasked with maintaining their own separate databases, thus creating inefficiencies and hindering collaboration. However, as industry continues to embrace digital transformation and AI, the need for unified data access at any level of the organization is becoming even more important.
Industrial data fabrics allow companies to access important data with connections to devices, sensors, cameras and DCS/SCADA systems, as well as break down data silos among these technologies. By doing this, data is not just accessible across cloud, edge and on-premises environments, it will also have the necessary context.
What’s more, an advanced industrial data fabric is aware of a data’s source location and associated context, so the challenging, manual step of moving data and applying context afterward is eliminated. For data to be effective across the organization, it’s important to know where it comes from, when it was collected, what sensors it represents and more.
With the right data management strategy, hyper-connectivity across the organization can be achieved, without maintenance and cybersecurity headaches. As industry continues to take steps toward improving its cybersecurity posture and accelerating digital transformation initiatives, streamlined and centrally managed data paradigms will play an increasingly important role.
Companies prioritizing data management best practices at the data fabric level will shrink their attack surface and be better equipped to rely on their data to make insight-driven decisions about the business.
Dr. Nina Schwalb is vice president, Inmation Industrial Data Fabric, at Emerson’s Aspen Technology business.
John Lewis is to host Topshop in 32 of its 36 stores from February as the clothing brand’s only national stockist on UK high streets, in a drive to attract younger shoppers and their mums.
Peter Ruis, the managing director of the staff-owned department store chain, said Topshop – and Topman, which is to go into six outlets – would “really exemplify the new John Lewis”, as it tries to broaden its appeal with more fashion, home and beauty brands, ranging from Fenty to the Conran Shop.
He said Topshop was “a brand that is going to resonate with our Gen Zs [those in their late teens and early 20s] and our Gen Xers and everyone in between”.
Ruis said Generation X – those in their 40s and 50s – were more interested in fashion than their parents and “grew up with Topshop” and now had the money to buy it, while the younger generations were interested in 90s revival fashion.
“I think the best fashion brands are multigenerational. I think that’s always been the case,” he said, suggesting Topshop would “bring more of that family day out” to the retailer. Ruis said younger customers were already shopping in its beauty halls alongside their parents and buying brands such as Mango but this would bring a new attraction.
He said Topshop would have a specially created area in stores staffed by handpicked John Lewis staff and was “going to have a lot of energy”.
Michelle Wilson, the managing director of Topshop, said John Lewis had picked a selection of 120 items suitable for its shoppers, including denim, footwear and faux leather jackets, with the most expensive item a £250 suede jacket.
She said shoppers “absolutely want to see us back in stores” and wanted to “touch and feel and try on product”, with John Lewis being “a name trusted by millions”.
Ruis said John Lewis, which is due to reveal its half-year results next week, was not ditching its mainstream customers.
“We are not just a typical luxury fashion and beauty department store. We’re one that sells you everything in your life,” he said, pointing to the chain’s very large market share in sales of cots and gear for babies. He said this broad appeal positioned it well to outlive the difficulties that led to the rivals Debenhams and Beales disappearing from high streets and House of Fraser significantly contracting.
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He said the retailer was “super positive” about trading in this year’s key Christmas period as its customer surveys indicated consumers were “more optimistic than they’ve been for a very long time”, partly thanks to mortgage rate decreases. “We’re seeing big-ticket items come back with a vengeance,” he added, referring to strong sales of sofas and mattresses in particular.
Last year’s return of the group’s now 100-year-old price promise Never Knowingly Undersold had gone better than expected, Ruis said, helping to pull in shoppers searching for goods online.
Paul Kirby
Image source, Reuters
Image source, SPUTNIK/KREMLIN/EPA/Shutterstock
