Cricket’s biggest stage is set to return to southern Africa, and Cricket South Africa (CSA) has decided to get a head start on preparations, confirming the eight venues across the country that will host 44 of the 54 matches in the ICC Men’s Cricket World Cup 2027, with the remainder split between Zimbabwe and Namibia.
The venues shortlisted for South Africa are Johannesburg, Pretoria, Cape Town, Durban, Gqeberha, Bloemfontein, East London, and Paarl—each carrying its own slice of the nation’s cricketing heritage.
For South Africa and its fans, the 2027 edition of the ODI World Cup marks a significant homecoming. The last ICC men’s tournament the country staged was the Champions Trophy in 2009, while the World Cup itself last came to their shores in 2003. More recently, South Africa hosted the 2023 ICC Women’s T20 World Cup.
The country also boasts the honour of launching the very first ICC Men’s T20 World Cup in 2007—a tournament that has since grown into one of the game’s biggest spectacles.
For Pakistan fans, the announcement is particularly relevant given the team’s long history of dramatic contests in African conditions—from their 2003 World Cup campaign to their famous T20 World Cup heartbreak in Johannesburg in 2007. With Pakistan expected to shake off their rust by 2027, all eyes will be on how the side adapts to pitches and conditions across South Africa, Zimbabwe, and Namibia.
The ICC Men’s Cricket World Cup 2027 promises not just a return to one of cricket’s most iconic regions, but also a stage where the next generation of stars—from Pakistan and beyond—will try to etch their names into the sport’s history.
Grimes and Anyma have reportedly called it quits after a year of dating.
On Thursday, an insider confirmed to People magazine that the Canadian singer and the Italian DJ have parted ways romantically but remain good friends and collaborators.
“Anyma and Grimes have amicably parted ways but still remain good friends and collaborators,” the source said.
The confidant further revealed to the outlet that the former couple is still planning to “release new music together soon.”
For those unversed, Grimes went Instagram official with Anyma in March 2024. At that time, she posted loved-up photos of herself with the music producer.
“Beauty and the Beast,” she captioned the post.
Before they went public with their relationship, Grimes and Anyma teamed up on her 2023 song, Welcome to the Opera.
Grimes was previously in a relationship with Elon Musk. She shares three children with a Tesla owner.
Anyma was previously married to model Vittoria Ceretti from 2020 to 2023. The Italian model is currently dating actor Leonardo DiCaprio.
On the work front, Grimes recently released her latest song, IDGAF, while Anyma dropped his new album, The End of Genesys, in May.
8 more Palestinians martyred by Israeli forces in Gaza RADIO PAKISTAN
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Reported impact snapshot | Gaza Strip, 20 August 2025 at 15:00 OCHA
One of the most fundamental processes in all of biology is the spontaneous organization of cells into clusters that divide and eventually turn into shapes – be they organs, wings, or limbs.
Scientists have long explored this enormously complex process to make artificial organs or understand cancer growth – but precisely engineering single cells to achieve a desired collective outcome is often a trial-and-error process.
Harvard applied physicists consider the control of cellular organization and morphogenesis to be an optimization problem that can be solved with powerful new machine learning tools.
In new research published in Nature Computational Science, researchers in the John A. Paulson School of Engineering and Applied Sciences (SEAS) have created a computational framework that can extract the rules that cells need to follow as they grow, in order for a collective function to emerge from the whole.
The computer learns these “rules” in the form of genetic networks that guide a cell’s behavior, influencing the many ways cells chemically signal to each other, or the physical forces that make them stick together or pull apart.
Currently a proof of concept, the new methods could be combined with experiments to allow scientists to understand and control how organisms develop from the cellular level.
The research was co-led by graduate student Ramya Deshpande and postdoctoral researcher Francesco Mottes. The senior author was Michael Brenner, Catalyst Professor of Applied Mathematics and Applied Physics at SEAS.
Automatic differentiation
The search for rules that cells must follow was enabled by a computational technique called automatic differentiation. This method, which forms the backbone of training deep learning models in artificial intelligence, consists of algorithms designed to efficiently compute highly complex functions. Automatic differentiation allows the computer to detect the precise effect that a small change in any part of the gene network would have on the behavior of the whole cell collective.
For the last several years, Brenner’s team has been applying such algorithms to problems beyond neural networks, including designing self-assembling colloid materials, improving fluid dynamics simulations, or engineering certain types of proteins.
Deshpande said the principles from the paper could help guide follow-up experiments on physical systems of cells.
Once you have a model that can predict what happens when you have a certain combination of cells, genes, or molecules that interact, can we then invert that model and say, ‘We want these cells to come together and do this particular thing. How do we program them to do that?’”
Ramya Deshpande, Graduate Student, Harvard John A. Paulson School of Engineering and Applied Sciences
Mottes said that by enabling the scaling of physics-based systems biology models, automatic differentiation offers a promising path toward achieving the predictive control needed to, in the distant future, engineer the growth of organs – the holy grail of computational bioengineering.
“If you have a model that is predictive enough and calibrated enough on experimental data, the hope is that you can just say, for example, ‘I want a spheroid with these characteristics. How should I engineer my cells to achieve this?’” Mottes said.
Source:
Harvard John A. Paulson School of Engineering and Applied Sciences
Journal reference:
Deshpande, R., et al. (2025). Engineering morphogenesis of cell clusters with differentiable programming. Nature Computational Science. doi.org/10.1038/s43588-025-00851-4
Analysis of the impact of inflammatory and nutritional markers on prognostic factors in lung cancer patients
Univariate analysis
This study ultimately included 500 patients with lung cancer, comprising 178 patients in the survival group and 322 patients in the deceased group. Patients in the survival group had significantly higher levels of lymphocyte count, serum albumin, hemoglobin, prognostic nutritional index (PNI), lymphocyte-to-monocyte ratio (LMR), hemoglobin-to-red cell distribution width ratio (HRR), hemoglobin-albumin-lymphocyte-platelet (HALP) score, albumin-to-globulin ratio (ALB/GLB), as well as a greater proportion of stage I cases and patients with high-to-moderate tumor differentiation. In contrast, patients in the deceased group exhibited significantly higher levels of serum globulin, age, systemic immune-inflammation index (SII Log), platelet-to-lymphocyte ratio (PLR Log), neutrophil-to-lymphocyte ratio (NLR), and a higher proportion of cases with distant metastasis, stage IV disease, Eastern Cooperative Oncology Group performance status (ECOG PS) ≥ 2, and poorly differentiated tumors. Table 1.
Table 1 Univariate analysis of clinical and laboratory indicators and poor prognostic factors in lung cancer Patients.
LASSO regression analysis and risk prediction formula
In this study, mortality in lung cancer patients was used as the dependent variable. Independent variables included clinical stage (coded as 1 = Stage I, 0 = Stage II/III/IV), differentiation grade (coded as 1 = low differentiation, 0 = moderate/high differentiation), ECOG PS (coded as 1 = ECOG PS 0–1, 0 = ECOG PS ≥ 2), serum albumin (measured value), LMR (measured value), HRR (measured value), ALB/GLB (measured value), and age (measured value).LASSO regression analysis identified ECOG PS 0–1, ALB/GLB, and age as independent prognostic factors for lung cancer. ECOG PS 0–1 and higher ALB/GLB levels were protective factors, while age was a significant risk factor. Specifically, the analysis indicated that each additional year of age increased the risk of mortality by approximately 7%.Table 2. The following formula was provided to calculate the mortality risk prediction score for individual patients: Exp(x) = [−0.94909] + [−0.47464 × 1(Stage I)] + [0.54761 × 1(Low differentiation)] + [−0.85073 × 1(ECOG PS 0–1)] + [−0.00427 × Serum albumin] + [−0.04974 × LMR] + [−0.28638 × HRR] + [−0.98366 × ALB/GLB] + [0.06838 × Age].The probability of mortality is calculated as: Probability = Exp(x)/[1 + Exp(x)].
Table 2 LASSO regression analysis of prognostic risk factors in patients with lung Cancer.
Development of a risk prediction model
Feature selection using LASSO regression for dimensionality reduction
In this study, LASSO regression analysis with 10-fold cross-validation was used to select the optimal log(λ) values. When log(λ) was − 2.8203 and − 3.5645, the data demonstrated stability and statistical significance. Figure 2 shows the coefficient distribution curve for different log(λ) values. Figure 3 illustrates the stepwise feature selection process, reducing 46 variables to 1. Each curve represents the trajectory of different predictor coefficients as the parameter changes.The optimal eight variables selected for constructing the lung cancer risk prediction model based on inflammation and nutrition markers were: age, Stage I, low differentiation, ECOG PS 0–1, serum albumin, LMR, HRR, and ALB/GLB.Age was identified as an independent risk factor for poor prognosis, with increasing age associated with worse outcomes. Patients with Stage I disease and ECOG PS 0–1 had better prognoses. In contrast, low differentiation, decreased serum albumin levels, and lower ALB/GLB were associated with worse outcomes. Elevated LMR and HRR were linked to improved prognosis.Table 3. Figure 2. Figure 3.
Table 3 Multivariate analysis of prognostic factors in two independent Models.
Fig. 2
Cross-validation of LASSO regression analysis. Note: The vertical axis represents the cross-validation error, which serves as a metric for assessing model goodness-of-fit. A smaller value indicates better model fit. The lower horizontal axis corresponds to log(λ), where λ is the regularization parameter that controls the complexity of the model. The upper horizontal axis denotes the number of variables retained at different log(λ) values. The two vertical dashed lines indicate the optimal log(λ) value and the log(λ) value within one standard error. Specifically, the upper horizontal axis value corresponding to the left dashed line represents the number of selected variables at the optimal log(λ) value.
Fig. 3
Variable selection path in LASSO regression. Each line in a different color represents a variable. The bottom x-axis corresponds to log(λ) values, while the top x-axis indicates the number of nonzero coefficients (variables) in the model for the respective log(λ) values. The log(λ) value controls the strength of regularization in the model: smaller log(λ) values correspond to weaker regularization, allowing more variables to enter the model, whereas larger log(λ) values correspond to stronger regularization, enhancing the model’s robustness to noise but shrinking many variable coefficients to zero.As the log(λ) value increases (from smaller to larger values), the model complexity decreases, with many variable coefficients gradually shrinking to zero or becoming exactly zero. However, the coefficients of certain variables remain nonzero throughout the process. This observation suggests that the model identifies these variables as more critical features, enabling dimensionality reduction or the selection of the most important predictors.
Near-Zero variance test of training data
To further refine variable selection and understand their local characteristics and distribution patterns, a near-zero variance test was conducted on multiple variables. The results showed that patient age, ALB/GLB, and ECOG PS scores (≥ 2 and 0–1) were evenly distributed, suggesting their relevance to prognosis. The proportions of LMR, HRR, and ALB/GLB were 93.79%, 94.99%, and 98.20%, respectively, indicating high individual variability among these variables.Table 4.
Table 4 Nearest neighbor variance analysis of clinical Variables.
Collinearity test on training data (Stepwise VIF Selection)
To improve the predictive performance of the model and address irrelevant variables, a collinearity test (VIF) was performed to further refine the feature selection. Variables such as Stage II, moderate/high differentiation, and ECOG PS ≥ 2 were stepwise excluded. The retained variables included Stage I, Stage III, Stage IV, low differentiation, ECOG PS 0–1, serum albumin (g/L), LMR, HRR, ALB/GLB, and age.The analysis identified age, clinical stage, low differentiation, ECOG PS 0–1, serum albumin levels, LMR, HRR, and ALB/GLB as independent prognostic factors.Table 5.
To identify the most relevant feature variables, recursive feature elimination (RFE) was performed. The selected variables included Stage I, low differentiation, ECOG PS 0–1, serum albumin (g/L), LMR, HRR, ALB/GLB, and age.The analysis revealed that LMR had the most significant predictive performance. Serum albumin, ALB/GLB, and HRR also demonstrated strong predictive capabilities. Age, Stage I, low differentiation, and ECOG PS 0–1 showed moderate predictive value.Table 6.
Table 6 Analysis of Cross-Validated performance metrics for all Predictors.
Variable importance
Finally, this study systematically evaluated the factors influencing prognosis and identified age as the most critical determinant. The factors ranked in descending order of impact were ECOG PS 0–1, ALB/GLB, poor differentiation, stage I, HRR, LMR, and serum albumin. Table 7.
Table 7 Analysis of predictor Importance.
Model evaluation
ROC curve (Internal validation performed 500 Times)
The performance of the model was evaluated using the ROC curve, with an area under the curve (AUC) of 0.7652 (95% CI: 0.7246–0.8029) and an accuracy of 0.711 (95% CI: 0.669–0.751). The model demonstrated high sensitivity (0.847) and moderate specificity (0.466), indicating good discriminatory power, making it suitable for preliminary disease screening. The positive predictive value (0.741) exceeded the negative predictive value (0.629), suggesting the model is more reliable in identifying positive cases.Internal validation was conducted using 500 bootstrap resamples. Calibration was assessed with isotonic regression fitting. The results showed an F1-score of 0.791 (range: 0–1, with higher values indicating better balance), confirming that the model achieved a good trade-off between precision and recall. These findings highlight the model’s strong predictive accuracy, good sensitivity, and reliable calibration, demonstrating its overall predictive and fitting performance.Table 8. Figure 4.
Table 8 Analysis of performance metrics for predictive Models.
Fig. 4
Optimal cutoff point
The ROC cut-off points, sensitivity, and specificity for eight inflammation-nutritional markers, including NLR, PLR, SII, LMR, PNI, HALP, HRR, and ALB/GLB, are presented in Supplementary Table S1. Users can select appropriate cut-off points based on their clinical context to predict future outcomes (see Supplementary_Table_S1.pdf).
Model calibration
The predictive performance of the model was evaluated using a visual calibration curve. The calibration curve closely aligned with the ideal curve, with a slope of 1. This indicates that the risk prediction model has good calibration performance and can provide reliable risk estimates. Figure 5.
Fig. 5
Calibration plot for the predictive model in the training dataset.
Clinical utility of the model
The decision curve analysis demonstrated that the predictive model (PRED.MODEL1) maintained stable performance across various risk thresholds. In the low-risk range (0.2–0.3), the model effectively identified high-risk patients requiring intervention while reducing overtreatment. In the moderate-risk range (0.3–0.6), it performed optimally, balancing treatment benefits with potential risks. Even in the high-risk range (0.6–0.8), the model retained good discriminatory ability, aiding in the identification of patients needing aggressive intervention.The model’s curve consistently remained above the reference line and was most prominent in the moderate-risk range (0.3–0.6). These findings indicate that the predictive model can effectively guide clinical decision-making. Figure 6.
Fig. 6
Decision curve analysis of PRED.MODEL1 for clinical decision-making.
Web-Based calculator for prognostic risk prediction in patients with lung cancer
We have developed a web-based calculator for predicting the prognostic risk of lung cancer patients. Instructions for Use: After accessing Risk Prediction Model for Overall Survival in Lung Cancer Patients, input the patient’s information as follows: Select “Yes” or “No” for “Stage I.“Select “Yes” or “No” for “Poor Differentiation.“Select “Yes” or “No” for “ECOG PS 0–1.“Enter the serum albumin level (g/L) in the designated field.Input the LMR, HRR, and ALB/GLB values in their respective fields.Enter the patient’s age in years.Once all fields are completed, click “Calculate Risk.” The probability of mortality for lung cancer patients will be automatically displayed at the bottom of the interface. For reference, see the webpage calculator interface in Fig. 7.
The Directorate General of Customs Valuation Karachi has revised customs values on the import of a wide range of medical items and equipment from China.
The directorate issued Valuation Ruling 2020 of 2025 on Thursday.
After a long period of eight years, the directorate has updated the values for the import of such equipment from China.
According to the ruling, the directorate had previously issued Valuation Ruling No. 1202 of 2017 for medical items and equipment under Section 25A of the Customs Act. As the valuation ruling was eight years old, an exercise for the re-determination of customs values of the subject goods was initiated, based on an analysis of import data, current market trends, and the difference in market prices and customs values.
Meetings were convened to re-evaluate the customs values of the subject goods. During these meetings, stakeholders submitted relevant documents along with their proposed values and samples, which were duly recorded.
The customs values of the subject goods have been determined under Section 25(9), read with Section 25(7), and Customs Rule 2001.
The rule provides that the methods of valuation employed under sub-section (9) of Section 25 of the Customs Act, 1969, may include those laid down in the sub-sections of the said section. However, reasonable flexibility in the application of such methods is in conformity with the aims and provisions of sub-section (9) of that section, it added.
Kylie Kelce admits THIS would be ‘one of the most beautiful’ parenting moments
Kylie Kelce spilling one of the situations that would make her emotional as a mom.
The podcast host recently invited Hacks star Hannah Einbinder on her podcast Not Gonna Lie.
During the talks, the two bonded over their love for the Philadelphia Eagles.
Kylie mentioned while they were chatting that it would be a proud moment for her if her daughters ever flipped a “double bird” at a rival fan,
“I tell my kids like curse words are grown up words [and] you’re not really supposed to use them,” the mom of four said. “If my child looked at an opposing team’s fan, and flipped them a double bird, I would cry tears of joy.”
However, Kylie noted that she won’t tell her kids to do so, but if it naturally happens, she will be proud.
“I’m not going to tell them to do it, and I’m not going to encourage it,” Kylie explained. “But, if their soul spoke to them, if all of the formal former Eagles fans who have passed on, god rest their souls, came down and imparted the wisdom onto her that she needed to double bird an opposing fan, it would be one of the most beautiful moments of my parenthood that I can even imagine.”
It is pertinent to mention that Kylie shared four daughters, Finnley, 4 months, Bennett, 2, Elliotte, 4, and Wyatt, 5 with former Philadelphia Eagles player.
Long-term results from a phase 3 study showed that adding a focal boost to standard external beam radiotherapy (EBRT) improved 10-year biochemical disease-free survival (DFS) and reduced biochemical failures by more than 50% in patients with localized intermediate- or high-risk prostate cancer. The approach also significantly improved DFS, local DFS, and nodal DFS but not overall survival or distant metastasis-free survival.
METHODOLOGY:
Patients with localized intermediate- or high-risk prostate cancer often require high doses of EBRT; however, whole-gland dose escalation often leads to “unacceptable” toxicities, the researchers explained.
To address this, the FLAME trial evaluated whether adding a focal boost to tumor lesions could improve biochemical DFS. Preliminary results indicated that dose escalation at the location of the primary tumor improved 5-year biochemical DFS, without additional toxicity. In this study, researchers reported 10-year follow-up results.
Overall, 571 patients with intermediate- or high-risk prostate cancer were randomly assigned to receive either standard EBRT of 77 Gy in 35 fractions to the whole prostate gland (n = 287) or EBRT with an integrated focal boost up to 95 Gy to MRI-visible lesions (n = 284).
The intention-to-treat analysis included 276 patients from the standard arm and 281 patients from the focal boost arm. Organ-at-risk constraints were prioritized over the focal boost dose during treatment planning.
The primary endpoint was 10-year biochemical DFS; other outcomes were DFS, local DFS, nodal DFS, distant metastasis-free survival, and overall survival. The median follow-up duration was 106 months.
TAKEAWAY:
At 10 years, biochemical DFS was 86% in the focal boost arm vs 71% in the standard arm (P < .001), corresponding to a reduction of more than 50% in biochemical failures (adjusted hazard ratio, 0.40; P < .001).
At 10 years, DFS was significantly better in the focal boost arm (81% vs 67%; P < .001), as were local DFS (95% vs 86%) and nodal DFS (94% vs 85%).
The dose-response analysis revealed a strong relationship between radiation dose and biochemical failure, with a biochemical failure rate of about 5% for doses exceeding 90 Gy.
No statistically significant differences in distant metastasis-free survival (P = .17) or overall survival (P = .77) were observed between the treatment arms; however, a dose-response relationship was observed, with higher focal doses linked to reduced distant metastatic failure.
IN PRACTICE:
The 10-year results demonstrated the “sustained benefit of focal boosting for patients with intermediate- and high-risk [prostate cancer] on their [biochemical disease-free survival],” the authors wrote. Moreover, “in comparison with other therapy options (eg, hormonal therapy, brachytherapy), there is no additional treatment-related toxicity, but 15% more men can be spared the potential burden of [prostate cancer] recurrence.”
SOURCE:
The study, led by Karolina Menne Guricova, MSc, the Netherlands Cancer Institute in Amsterdam, the Netherlands, was published online in Journal of Clinical Oncology.
LIMITATIONS:
The study was underpowered to detect differences in additional outcomes. Imaging standards and contouring guidelines evolved during the study, leading to heterogeneity in focal boost dose delivery.
DISCLOSURES:
The study received funding support through grants from the Dutch Cancer Society. Some authors reported receiving honoraria or research funding from and having other ties with various sources. Additional disclosures are noted in the original article.
This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
KARACHI – Sindh Minister for Energy, Planning and Development, Syed Nasir Hussain Shah on Thursday, paid a surprise visit to the Head Office of K-Electric on public complaints and directed KE officials to take immediate and practical measures for the redressal of citizens’ grievances in order to provide relief to the public.
The minister, according to a statement issued here, expressed serious concern over prolonged load-shedding and said that due to rains, tripping of feeders causes severe difficulties for citizens; therefore, KE staff and machinery must remain mobilized on an emergency basis. He instructed KE to prepare a comprehensive plan for timely repair and restoration of feeders and emphasized that complaint centers should be fully activated to promptly resolve public issues. Nasir Shah stated that uninterrupted power supply is a fundamental right of the people while prolonged power outages are not only disrupting the daily lives of citizens but also severely affecting the supply and drainage of water from pumping stations.
KE officials assured the provincial minister that the company would further improve its services and utilize all available resources to ensure timely redressal of complaints.
On this occasion, the Sindh Energy Minister announced that he would once again pay a surprise visit to KE’s Head Office to review the measures taken for addressing public grievances.
