Category: 3. Business

ISLAMABAD: Pakistan’s economic recovery has been stronger than expected, yet the country has failed to seize an opportunity to put its recovery on a sustainable path due to the absence of bold and long-lasting reforms, according to the Washington-based Institute of International Finance (IIF).

In a special report, the IIF noted that while Pakistan has successfully rebuilt its economic buffers and secured financing, the gains will likely prove short-lived without comprehensive structural reforms, particularly in tax broadening, privatisation, and the resolution of circular debt.

The report emphasised that Pakistan has made little headway in these critical areas, particularly privatisation and energy-sector restructuring, with circular debt still unresolved. The IIF warned that these unresolved issues pose a significant risk to Pakistan’s economic outlook for FY26.

Notably, inflation has decreased significantly, allowing the State Bank of Pakistan (SBP) to cut its policy rate to 11pc since the easing cycle began in June 2024.

In addition, Pakistan posted its first current account surplus (0.5pc of GDP) since FY11, along with the highest primary balance surplus (2.4pc of GDP) in over two decades in FY25. These developments have resulted in sustained multilateral and bilateral support and improved credit ratings.

However, the IIF highlighted that despite these positive headlines, the economic situation is not as promising as it may seem. Geopolitical tensions, both regional and global, pose significant challenges for FY26, while domestic political instability, though subsiding, remains fragile. The relationship between the military establishment and the opposition PTI party remains tenuous, adding to the uncertainty.

Highlights structural weaknesses in tax broadening, privatisation and energy sector, undermining long-term stability

While fiscal and external buffers accumulated in FY24/25 have provided some relief, they remain limited. The $5bn increase in reserve assets has boosted the country’s import coverage to just 2.4 months, while the primary balance surplus has led to a slight reduction in total public sector debt, which remains high at around 67pc of GDP. These figures suggest that while short-term stability has been achieved, long-term sustainability remains uncertain.

The IIF also pointed out that the recent trade agreement with the United States, Pakistan’s largest export partner, could provide some support to the textile industry, though the benefits are expected to be modest. Agriculture, which accounts for nearly a quarter of GDP and employs 40pc of the workforce, will likely remain sluggish. The kharif season, covering key crops such as rice, sugarcane, cotton, and maise, has faced early water shortages followed by heavy monsoon rains, which could weigh heavily on harvests in the first half of FY26.

Furthermore, deadly flash floods have exacerbated the challenges, plunging Pakistan into its second major flooding crisis in three years. This could have serious implications for growth, as well as for the country’s external and fiscal balances.

Inflation, while improving, remains a concern. A sharp rise in food prices, caused by the floods, led to a 2.9pc month-on-month increase in headline inflation in July, the largest increase in two years. Core inflation remains sticky, hovering around 7pc in urban areas and 8pc in rural areas. Additionally, energy price adjustments (including higher gas tariffs, subsidy removals, and increased fuel costs) and new tax measures are expected to feed into inflation in the near term. As a result, the SBP paused interest rate cuts in June and July, and the IIF expects interest rates to remain on hold for an extended period, with inflation averaging 6.5pc in FY26.

On the external front, the IIF forecasted that Pakistan’s current account will be influenced by the normalisation of imports (particularly machinery, raw materials, and consumer goods). Exports will depend largely on the progress of the US-Pakistan trade deal, though the IIF remains sceptical of its impact on exports. In the absence of a sharp increase in commodity prices, the current account deficit is expected to stay modest (about 0.5pc of GDP in FY26), allowing some reserve accumulation, though import coverage will remain critically low at about 2.5 months.

The IIF also expressed concerns over Pakistan’s fiscal situation. Although the country’s fiscal deficit narrowed to 5.4pc of GDP in FY25 and total revenues grew by 35.6pc, much of this growth came from one-off factors, such as record profits from the State Bank of Pakistan.

The Federal Board of Revenue (FBR) fell short of its target by about 1pc of GDP, and the federal tax-to-GDP ratio remains stuck at around 10pc. For FY26, authorities are forecasting another large increase in tax revenues, but this may be difficult to sustain given the already high tax burden on the formal sector and the exclusion of the retail/wholesale sectors, which account for about 20pc of GDP, from the tax net.

On the expenditure side, reductions in subsidies (particularly on electricity) and net lending to public enterprises have helped, but total spending still rose by 18pc in FY25. The IIF also noted that recent tensions with India could lead to an increase in defence spending in FY26.

As a result, the authorities’ targets of a 3.9pc of GDP deficit and a 2.4pc primary surplus for FY26 appear overly ambitious. The heavy reliance on domestic financing is also a cause for concern, and the IIF warned that fiscal performance will remain a key test for the IMF programme, with vested interests complicating the progress of reforms.

Published in Dawn, August 23rd, 2025

Establishing a mouse model of EPS with rapid intra-abdominal adhesions

We aimed to develop a novel and more efficient mouse model of encapsulating peritoneal sclerosis (EPS), closely mimicking the condition observed in human patients. To achieve this, we combined several high-risk factors commonly associated with peritoneal dialysis-related EPS, including a 4.25% peritoneal dialysis solution (PD), a surgical hygiene solution (SHS) consisting of chlorhexidine gluconate and ethanol, and lipopolysaccharide (LPS) to replicate episodic peritonitis (Fig. 1A). While traditional models using SHS alone require up to 8 weeks for EPS formation [13], the addition of PD and LPS successfully accelerated the process, enabling significant intra-abdominal adhesion formation by the 3rd week.

Macroscopic evaluation revealed distinct differences between the experimental groups. Control mice showed smooth hepatic edges and well-expanded mesenteries with no visible adhesions (Fig. 1B). In contrast, the PD + LPS + SHS group exhibited prominent signs of adhesion, including thickened liver margins with fibrous deposits (Fig. 1B, yellow arrow) and extensive adhesions between the abdominal organs, particularly between the liver, intestines, and parietal peritoneum (Fig. 1B, blue arrow). Additionally, these mice displayed severe mesentery contraction and intestinal dilation, with some cases forming an abdominal “cocoon” structure, indicative of advanced EPS pathology (Supplemental Fig. 1). The adhesion scores of the PD + LPS + SHS group were significantly higher than those of the single control groups, demonstrating the robustness of the model in replicating EPS (Fig. 1C).

To assess the physiological impact of EPS, we monitored body weight and survival rates. Mice in the PD + LPS + SHS group showed significant weight loss compared to other groups, suggesting a progression toward malnutrition or cachexia, a common complication in EPS (Fig. 1D). Despite the severity of the disease, the survival rate in this group remained relatively high at 83.3%, allowing us to reliably conduct further analyses (Fig. 1E).

Since invasive exploration of the abdominal cavity is impractical in clinical settings, we employed ultrasonography to non-invasively assess the structural changes in the peritoneum, as commonly done in patients²⁰. Comparing to the control group (Fig. 1F-a, Supplementary Video 1), ultrasonography of EPS mice revealed key pathological features, including peritoneal thickening, calcification, and characteristic intestinal dilation (Figs. 1F-b and 1F-c), resembling the “concertina-like” appearance often observed in EPS patients. The mouse model also demonstrated strong adhesions between the parietal peritoneum and intestinal loops, which were confirmed by static and dynamic imaging (Fig. 1F-d, Supplementary Video 2). To validate the clinical relevance of our model, we compared these findings with ultrasonographic features from human EPS patients. The human subjects exhibited similar pathological traits, such as peritoneal thickening, calcification, and organ adhesions (Figs. 1F-f to 1F-h). These similarities between the mouse model and human disease underscore the utility of this model for studying EPS pathogenesis and evaluating potential therapeutic interventions. In summary, the combination of PD, LPS, and SHS successfully accelerated the development of EPS in mice, with key anatomical and pathological features closely mimicking human EPS. Given its efficiency and reproducibility, this model will be used for further studies aimed at understanding the mechanisms of EPS and testing potential therapeutic strategies.

EPS mouse model exhibits inflammation, fibrosis, and increased vascular density in peritoneum

To further characterize the pathological changes in our EPS mouse model, we performed detailed histopathological analyses of the peritoneum. As shown in Fig. 2A, cross-sectional images of the abdominal cavity revealed widespread and diffuse adhesions throughout the peritoneum in EPS mice. These adhesions formed dense, clot-like structures that encompassed multiple abdominal organs, closely resembling advanced EPS pathology observed in humans. Histological staining provided insight into the structural and cellular changes associated with EPS. Hematoxylin and eosin (H&E) staining indicated substantial thickening of both the parietal and visceral peritoneum in the EPS group, with marked infiltration of inflammatory cells (Fig. 2B, C). This infiltration signifies a heightened inflammatory response within the peritoneal tissues, which is a key feature of EPS progression. In addition, Masson’s trichrome staining highlighted extensive fibrotic deposition, further confirming that fibrosis is a central component of EPS pathology (Fig. 2B, C). Quantitative analysis of peritoneal thickness revealed a significant increase in EPS mice compared to controls, as depicted in Fig. 2G, H, reflecting the overall fibrotic burden in the disease. To explore the molecular drivers of this fibrotic response, we conducted immunohistochemical analyses, which demonstrated the upregulation of extracellular matrix (ECM) markers, including collagen type I alpha 1 (COL1A1), fibronectin (FN) and α-smooth muscle actin (α-SMA), in the peritoneum of EPS mice (Fig. 2D, Supplemental Fig. 2A). These markers are indicative of active fibroblast proliferation and matrix remodeling, key processes in tissue fibrosis.

Inflammation is known to play a critical role in EPS development^21,22. In consistent with these observations, our immunohistochemical analysis showed significant upregulation of pro-inflammatory cytokines, including interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α), within the peritoneal tissues of EPS mice (Fig. 2E, Supplemental Fig. 2B). Furthermore, enzyme-linked immunosorbent assay (ELISA) confirmed elevated expression of IL-6 in the peritoneal lavage fluid of EPS mice compared to controls (Fig. 2I), highlighting the systemic inflammatory milieu. Another critical aspect of EPS pathology is increased vascular density, often associated with chronic inflammation and fibrosis. In the EPS model, we observed a notable increase in blood vessel density in the visceral peritoneum, as demonstrated by CD31 staining (Fig. 2F, Supplemental Fig. 2C). Collectively, these results provide a comprehensive view of the severe inflammation, fibrosis, and angiogenesis occurring in the peritoneum of EPS mice. Importantly, these pathological changes closely mirror those reported in human EPS, validating the utility of this mouse model for studying disease mechanisms and potential therapeutic interventions.

Dynamic collagen deposition and inflammatory cell infiltration during EPS formation

To investigate the dynamic progression of intraperitoneal adhesion formation in EPS, we sacrificed mice at different time points and analyzed the histopathological changes in the peritoneum. On day 1, no noticeable infiltration of inflammatory cells or collagen fiber deposition was observed on the peritoneal surface. By day 7, inflammatory cells had begun infiltrating the peritoneum, accompanied by the initial deposition of collagen fibers (Fig. 3A). This early infiltration laid the groundwork for adhesion formation, with immune cells accumulating on the surface of fibers by day 14, and the fibrils progressively thickening and interconnecting to form adhesions. As the process advanced, collagen fibers continued to proliferate and gradually became the dominant structural component of the adhesions, while the infiltration of immune cells began to subside. By day 21, the adhesions had evolved into dense fibrous scar tissue, with minimal remaining immune cell infiltration (Fig. 3A). These findings closely mirror the pathological evolution observed in human EPS, where early inflammation gives way to excessive fibrotic deposition over time.

In addition to collagen depositions, H&E and Masson’s trichrome staining revealed significant infiltration of inflammatory cells within the adhesion regions (Fig. 3A). To determine the types of immune cells involved, we conducted immunohistochemical staining with markers for specific cell types at the conclusion of EPS molding (day 21). The markers used were F4/80 for macrophages, CD3 for T lymphocytes, and Ly6G for neutrophils (Fig. 3B). Among these, macrophages were found to be the predominant immune cells present within the adhesions, suggesting their key role in driving the fibrotic response (Fig. 3C). This was further supported by the extensive infiltration of macrophages in the parietal peritoneum (Supplemental Fig. 3), reinforcing the critical involvement of macrophages in the adhesion formation process. The graphical diagram in Fig. 3D illustrates the dynamic formation process of interorgan adhesions, depicting the transition from early inflammatory infiltration to the eventual deposition of fibrous scar tissue. The findings indicate the role of macrophage recruitment in the development of EPS, offering additional understanding of the cellular processes involved in adhesion formation within this disease model.

Transcriptomic alterations in peritoneal tissues of EPS mouse models

To gain insights into the transcriptomic alterations in the peritoneal tissues of EPS mice, we performed bulk RNA sequencing on the visceral peritoneum, using three biological replicates from both the Control and EPS groups (Fig. 4A). Our analysis identified a total of 3,900 upregulated genes and 4,397 downregulated genes in the EPS group compared to controls (adjusted p-value < 0.05). Notably, macrophage-associated genes such as Marco and Csf3r, along with chemokine-related genes, exhibited significant upregulation in the EPS group, underscoring the inflammatory nature of the disease (Supplemental Fig. 4).

To better understand the functional implications of these differentially expressed genes (DEGs), we performed Gene Ontology (GO) enrichment analysis. As shown in Fig. 4B, the upregulated genes in the EPS group were predominantly enriched in biological processes associated with immune activation, such as leukocyte migration, myeloid leukocyte activation, and interleukin production. These findings suggest that immune cell activation, particularly the infiltration of mononuclear macrophages, plays a central role in the progression of EPS. In addition, Gene Set Variation Analysis (GSVA) further supported this, with a marked increase in myeloid leukocyte activation scores in the EPS mice (Fig. 4C). This aligns with the observed increase in macrophage infiltration, indicating that these immune cells are key contributors to the ongoing inflammatory and fibrotic processes.

To gain further insight into the cellular composition of the peritoneal tissues, we utilized two publicly available single-cell RNA sequencing (scRNA-seq) datasets from mouse omentum and classified them into seven distinct clusters based on canonical marker genes (Fig. 4D) (GSE 136636, GSE176254). Differential expression analysis between the Control and EPS groups revealed that 63% of the marker genes identified from scRNA-seq overlapped with those from our bulk RNA-seq data (Fig. 4E). Using this dataset, we applied deconvolution analysis with the R package MuSiC to assess the relative proportions of different cell types. Our analysis showed a marked decrease in the proportion of mesothelial cells in the EPS group, suggesting a disruption in the mesothelial layer, which is a known contributor to adhesion formation by exposing adhesive fibrin clots to surrounding tissues²³. Conversely, we observed a significant increase in the proportion of fibroblasts in the EPS mice, which likely contributes to the excessive fibrous deposition seen in the disease. In terms of immune cell populations, both macrophages and T cells were significantly elevated in the EPS group, with macrophages being the most predominant immune cell type (Fig. 4F). To validate these findings, we performed flow cytometry on peritoneal tissues, confirming that the percentage of macrophages was significantly higher in EPS mice, corroborating the RNA-seq results (Fig. 4G). Furthermore, we examined the correlation between different immune cell types and the expression of the fibrosis marker Col1a1. Among the immune cell markers evaluated, F4/80, a macrophage marker, demonstrated the strongest positive correlation with Col1a1 expression (R² = 0.471, p = 0.042) (Fig. 4H). Concurrently, the proportion of macrophages detected by flow cytometry showed a significant positive correlation with COL1A1 expression in the peritoneum as measured by immunohistochemistry (R² = 0.9311, p < 0.0001), as presented in Fig. 4I, suggesting that macrophages are the main immune cell type involved in fibrotic deposition in EPS. These findings, when combined with our previous immunohistochemistry data, emphasize the pivotal role of macrophage infiltration in the formation of EPS and further highlight the synergistic relationship between immune cell infiltration and fibrotic deposition in this disease model.

Activation of the cGAS-STING pathway in mesothelial cells regulates macrophage chemotaxis

To investigate the mechanism underlying the significant macrophage infiltration observed in the EPS peritoneum, we reanalyzed our bulk RNA sequencing data. KEGG analysis revealed that genes upregulated in EPS mice were enriched in pathways related to cell chemotaxis and cytoplasmic DNA sensing, particularly the cGAS–STING pathway (Supplemental Fig. 5A–B), highlighting a potential link between immune cell recruitment and intracellular immune surveillance mechanisms (Fig. 5A). Additionally, GSVA demonstrated a strong positive correlation between the cytoplasmic DNA-sensing pathway and cytokine chemotaxis and inflammatory response pathways, further supporting the role of intracellular surveillance system in EPS progression (Fig. 5B, C).

Given that CCL2 is a well-established chemokine involved in macrophage recruitment^24,25, our qPCR validation confirmed that CCL2 exhibited the most pronounced expression change among chemokines in the peritoneal tissues of EPS mice (Supplemental Fig. 6A). ELISA analysis of the peritoneal lavage fluid revealed significantly higher levels of CCL2 in the EPS group compared to controls (Fig. 5D). Immunofluorescence also showed a marked increase in CCL2 expression in the parietal peritoneum, primarily co-localizing with Cytokeratin 7⁺ mesothelial cells (Fig. 5E, Supplemental Fig. 6B). These findings suggest that mesothelial cells are the primary source of CCL2 secretion in EPS, driving the chemotaxis of macrophages.

We then investigated the activation of the cGAS-STING pathway, which is known to mediate responses to cytoplasmic DNA. Both Western blot and immunofluorescence confirmed significant activation of the cGAS-STING pathway in EPS peritoneum, with STING co-localizing primarily with mesothelial cells (Fig. 5F, G, Supplement Supplemental Fig. 6C, D), suggesting that activation of the cGAS-STING pathway in mesothelial cells under EPS conditions may be responsible for promoting the chemotaxis of inflammatory cells, such as macrophages. After initial experiments to set up the in vitro EPS model (Supplemental Fig. 6E-H), we simulated the EPS environment by treating human peritoneal mesothelial cells (HPMCs) with LPS and SHS. Under these conditions, mesothelial cells exhibited cytoplasmic DNA leakage (Fig. 5H), consistent with cGAS-STING pathway activation. Western blotting and immunofluorescence confirmed the activation of STING and its downstream proteins in mesothelial cells upon EPS stimulation, while treatment with the STING inhibitor H151 partially mitigated this effect (Fig. 5I, J). qPCR analysis further demonstrated that the expression of inflammatory-related genes was significantly upregulated in mesothelial cells under EPS stimulation, and this effect was also suppressed by H151 (Fig. 5K). We next examined the role of CCL2 in this process. RT-qPCR and cellular immunofluorescence showed that both CCL2 gene expression and CCL2 protein levels were significantly elevated in mesothelial cells under EPS stimulation, and these increases were attenuated by H151 (Fig. 5L, M). The secretion of CCL2 in the cell supernatant was also significantly higher in EPS-stimulated mesothelial cells (Fig. 5N). STING agonists ADU-S100 also significantly induced CCL2 expression (Supplemental Fig. 6I, J), verifying the role of STING activation in promoting CCL2 secretion.

To directly assess the impact of mesothelial cell activation on macrophage migration, we performed a transwell co-culture assay, where mesothelial cells were co-cultured with macrophages (Fig. 5O). EPS-stimulated mesothelial cells significantly promoted macrophage migration (Fig. 5P, Q), while treatment with H151 partially alleviated this effect. These findings demonstrate that activation of the cGAS-STING pathway in mesothelial cells leads to the secretion of CCL2, which in turn induces macrophage chemotaxis and migration. Collectively, our results highlight the critical role of mesothelial cells in sensing EPS-related stimuli and activating the cGAS-STING pathway, thereby driving macrophage recruitment through CCL2 secretion. Inhibition of STING effectively reduces both CCL2 production and macrophage migration, offering a potential therapeutic approach for mitigating macrophage-driven inflammation in EPS. Additionally, we observed that anti-CCL2 intervention also significantly attenuated in vitro EPS-induced macrophage migration, again indicating that CCL2 acts as the predominant cytokine driving macrophage chemotaxis in this microenvironment (Fig. 5R, S).

Inhibition of cGAS-STING activation ameliorates EPS formation in mice

To investigate whether inhibiting the cGAS-STING pathway could reduce the severity of EPS, we administered the STING inhibitor H151 via intraperitoneal injection in mice (Fig. 6A). Western blot and immunofluorescence analyses confirmed that H151 effectively suppressed the activation of STING and downstream signaling proteins in the peritoneal tissues (Fig. 6B), validating its ability to block STING signaling in vivo. In parallel, H151 treatment significantly lowered the expression of CCL2 in peritoneal tissue (Fig. 6C, Supplemental Fig. 7A) and in the peritoneal lavage fluid (Fig. 6D). This reduction in CCL2 was associated with a decrease in macrophage infiltration on the peritoneal surface, observed through immunohistochemistry (Fig. 6E, F). Correlation analysis between the CCL2 concentration in peritoneal lavage fluid and the F4/80 infiltration area showed a significant positive correlation (Fig. 6G). These results indicate that STING-mediated chemotaxis, driven by CCL2, plays a central role in recruiting macrophages during EPS progression. Therapeutically, H151 treatment markedly improved the pathological features of EPS. Mice treated with H151 showed a significant reduction in adhesion scores during gross assessments (Fig. 6H, I), reflecting decreased intra-abdominal adhesions. Histological staining also revealed a notable reduction in collagen fiber deposition in the adhesion regions (Fig. 6Ja-b and 6K). The thickness of the parietal peritoneum, a hallmark of fibrosis in EPS, was significantly decreased in H151-treated mice (Fig. 6J-c and L), indicating a direct impact on fibrotic progression. Immunohistochemistry showed that ECM-related proteins (COL1A1, fibronectin, α-SMA) (Fig. 6d-f), inflammatory markers (IL-1β, IL-6, TNF-α), and pro-angiogenic VEGF were significantly reduced in the H151-treated group (Fig. 6M), supporting the inhibitor’s anti-adhesion effect. In summary, these findings suggest that inhibiting cGAS-STING activation with H151 reduces CCL2 secretion and macrophage recruitment, ultimately mitigating the excessive collagen deposition and adhesion formation characteristic of EPS. This highlights the potential of targeting the STING pathway as a therapeutic strategy for EPS.

IRF3 as the key transcription factor promoting CCL2 secretion in mesothelial cells

Cyclic GMP-AMP synthase (cGAS) detects cytosolic DNA during cellular stress and activates the adaptor protein STING, which triggers immune responses by activating the downstream transcription factor IRF3^26,27,28. We investigated whether the activation of STING in mesothelial cells under EPS condition regulates CCL2 secretion via IRF3. Using the Univariate Linear Model (ULM) method in the DecoupleR package, we analyzed our bulk RNA-seq data and found that IRF3 transcriptional activity was significantly elevated in the EPS group (Fig. 7A). This increase in IRF3 activity aligns with our observation of increased phospho-IRF3 levels in mesothelial cells under EPS condition (Fig. 5I). Among the genes predicted to be regulated by IRF3, CCL2 and CCL5 were the most upregulated in the EPS group (Fig. 7B), with CCL2 had the higher p-value (p < 10^-6). To further investigate how IRF3 promotes CCl2 expression, we used the JASPAR database to analyze the transcription factor binding sites within the CCL2 promoter and identified a high-scoring potential IRF3 binding site (Fig. 7C). This suggested a direct interaction between IRF3 and the CCL2 promoter region (Fig. 7D). To test this possibility, we conducted a chromatin immunoprecipitation (ChIP) assay, which validated that IRF3 indeed binds to the promoter of the CCL2 gene (Fig. 7E, F, Supplemental Fig. 8). These findings demonstrate that under EPS condition, IRF3 binding to the CCL2 promoter facilitates increased CCL2 production and secretion. Thus, IRF3 serves as a key transcription factor driving CCL2 secretion in mesothelial cells in response to EPS. IRF3 knockdown significantly reduced both the transcription and secretion of CCL2 in response to stimulation with a STING agonist (Supplemental Fig. 6I, J) and EPS-conditioned media (Fig. 7G, H). Furthermore, this genetic intervention markedly inhibited macrophage migration, as shown by transwell assays (Fig. 7I, J). These results provide functional evidence that IRF3 is a key transcriptional regulator of CCL2 in EPS-associated mesothelial responses.

Clinical relevance of STING activation and EPS

For a clinical investigation of the cGAS-STING pathway, we collected peritoneal tissues, including adhesion tissues, from patients with EPS and healthy controls. Immunofluorescence staining revealed significant activation of the cGAS-STING pathway in mesothelial cells from the peritoneal surface of EPS patients (Fig. 8A), along with a marked increase in CCL2 expression (Fig. 8B). Given that a significant number of mesothelial cells are shed into the peritoneal dialysis fluid under EPS condition, we also prepared smears of the shed cells from the peritoneal dialysis effluent for immunofluorescence staining, which yielded results consistent with the peritoneal tissue (Fig. 8C–H). Additionally, ELISA measurements confirmed that the concentration of CCL2 in the peritoneal dialysis effluent was significantly higher in EPS patients compared to controls (Fig. 8I), so as the cGAMP, which serves as an indicator of STING activation (Fig. 8J). Furthermore, there is a positive correlation between CCL2 and cGAMP concentrations (Fig. 8K). These clinical findings corroborate the link between STING activation and increased CCL2 levels in EPS, further supporting the role of the cGAS-STING pathway in driving macrophage chemotaxis and inflammation.

KARACHI:

The State Bank of Pakistan (SBP) injected a staggering Rs1.4 trillion into the financial system on Friday through dual open market operations, using both conventional and Shariah-compliant instruments to manage the huge liquidity demand.

In the conventional reverse repo operation, the SBP injected Rs1.037 trillion against total offers of Rs1.150 trillion. The central bank accepted Rs76 billion at 11.06% for seven-day tenor and Rs962 billion at a cut-off rate of 11.04% for 13-day tenor, with pro rata allotment applied where necessary.

Simultaneously, the SBP conducted a Shariah-compliant Mudarabah-based injection of Rs363 billion, accepting all offered bids – Rs241 billion at 11.14% for seven days and Rs122 billion at 11.13% for 13 days.

Moreover, the Pakistani rupee registered a marginal gain against the US dollar, appreciating 0.01% in the inter-bank market. At close, the local currency settled at 281.90, up two paisa compared with the previous day’s close at 281.92. This marked the rupee’s 11th consecutive session of gains against the greenback.

According to Ismail Iqbal Securities, the rupee has now appreciated 0.66% in the current fiscal year to date, although it remains down 1.19% on a calendar-year-to-date basis.

Analysts at AKD Securities highlighted that the rupee has strengthened for the fifth consecutive week, reflecting improved sentiment amid stability in foreign exchange reserves and remittance inflows.

Meanwhile, gold prices in Pakistan fell, contrary to movements in the international market, where bullion rebounded after comments from US Federal Reserve Chair Jerome Powell fueled expectations of a September rate cut at the Jackson Hole symposium.

According to the All Pakistan Sarafa Gems and Jewellers Association, the price of gold declined Rs1,500 to settle at Rs355,700 per tola, while the rate for 10 grams dropped Rs1,286 to Rs304,955. A day earlier, gold had gained Rs2,000 to close at Rs357,200 per tola.

Internationally, spot gold was up 0.7% at $3,362.53 per ounce by 10:26 am EDT (1426 GMT), while US gold futures were 0.8% lower at $3,408.20, Reuters reported.

Market analysts noted that initially gold traded in a narrow $25-40 range, with little momentum amid lack of progress in the geopolitical situation such as the Russia-Ukraine conflict. However, Powell’s remarks that future data could warrant interest rate cuts triggered renewed buying, lifting prices to the high of $3,380.

Interactive Commodities Director Adnan Agar said that the market remained range bound but faced strong resistance at the $3,400 level. “If gold breaks this barrier, the next target is projected around $3,450,” he said.

Agar added that expectations of a downward shift in US interest rates, combined with persistent geopolitical risks, continue to bolster the metal’s appeal as a safe-haven asset.

With US rates having remained unchanged for an extended period, the possibility of an imminent cut has reinforced bullish sentiment, though traders caution that volatility will likely persist in the short term.

Participating laboratories

Coordinating laboratory: Teagasc Food Research Centre, Moorepark, Fermoy, Co Cork P61 C996, Ireland.

Participating laboratories:

• Laboratory of Food Chemistry and Biochemistry, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, P.O. Box 235, 54124, Thessaloniki, Greece

• Global Oatly Science and Innovation Centre, Rydbergs Torg 11, Space Building, Science Village, 22 484 Lund, Sweden

• Laboratory of Food Technology, Department of Microbial and Molecular Systems (M2S), KU Leuven, Kasteelpark Arenberg 23, PB 2457, 3001, Leuven, Belgium

• INRAE, Institut Agro, STLO, 35042 Rennes, France

• School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom

• Nofima AS, Norwegian Institute of Food, Fisheries and Aquaculture Research, PB 210, N-1433, Ås, Norway

• Center for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark

• Department of Horticulture, Martin-Gatton College of Agriculture, Food and Environment, University of Kentucky, Lexington, Kentucky, USA

• Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Italy

• Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WG Wageningen, The Netherlands

• Quadram Institute Bioscience, Rosalind Franklin Road, Norwich Research Park, Norwich, NR4 7UQ, United Kingdom

• Department of Food Engineering, Faculty of Engineering, Ege University, 35100, İzmir, Türkiye

Materials

The chemicals and four test products used in the ring study are presented in (Table 3). They were ordered by the coordinating laboratory, aliquoted and shipped to each of the participating laboratories. All laboratories received aliquots from the same batch of each product, with the exception of 3,5-dinitrosalicylic acid (DNSA) which came from two different lots. Prior to shipping, calibration curves established with solutions prepared from both of these lots were compared, and showed nearly equivalent results (Figure S1 in Supplementary material-Section “Protocol implementation at each laboratory”).

Equipment needed

The list of equipment required is provided as guidance below.

Preparation of reagents and enzyme solutions

Vortex mixer, pH meter with glass electrode, heating/stirring plate, incubator.

Enzyme assay

Water-bath or thermal shaker (e.g. PCMT Thermoshaker, Grant Instruments, United Kingdom) for enzyme–substrate incubations at 37 °C. Boiling bath (e.g. SBB Aqua 5 Plus, Grant Instruments, United Kingdom) or thermal shaker (e.g. PCMT Thermoshaker, Grant Instruments, United Kingdom) suitable for use at 100 °C. Spectrophotometer (e.g. Shimadzu UV-1800 Spectrophotometer, Shimadzu Corporation, Japan) or plate reader (e.g. BMG Labtech CLARIOstar Plus, BMG Labtech, Germany).

Basic materials

Volumetric flasks, heatproof bottle with lid (e.g. Duran bottle), magnetic stirrer, timer, thermocouple, safe lock microtubes (2 or 1.5 mL), heat (and water) resistant pen or labels for the microtubes, disposable standard cuvette or disposable polystyrene 96-well plate.

Preparation of reagents and enzymes

20 mM Sodium phosphate buffer (with 6.7 mM sodium chloride, pH 6.9 ± 0.3)

Prepare a stock solution by dissolving 1.22 g NaH₂PO₄ (anhydrous form), 1.38 g Na₂HPO₄ (anhydrous form) and 0.39 g NaCl in 90 mL purified water and make up the volume to 100 mL. Before use, dilute 10 mL of stock solution to 95 mL with purified water. Confirm that the pH of the buffer, when heated to 37 °C, is within the required working range (pH 6.9 ± 0.3). If needed, adjust the pH, using 1 M NaOH or HCl as required, before making up the volume to 100 mL.

Maltose calibrators

Prepare a 2% (w/v) maltose stock solution in phosphate buffer. Prepare a calibrator series by diluting the maltose stock solution in phosphate buffer as indicated in Table S2 (Supplementary Material – Section “Protocol implementation at each laboratory”). Store in the fridge (or freezer if not for use during the same day).

Colour reagent (96 mM DNSA with 1.06 M sodium potassium tartrate)

Dissolve 1.10 g of DNSA in 80 mL of 0.50 M NaOH at 70 °C in a glass beaker or bottle (partly covered to limit evaporation) on a pre-heated heat/stir plate with continuous stirring and temperature monitoring (e.g. using a thermocouple). Once the DNSA is fully dissolved, add 30 g of sodium potassium tartrate and continue stirring until it dissolves. Remove from heat and wait until the solution cools to room temperature. Bring to 100 mL with purified water. Store at room temperature protected from light for up to 6 months. If precipitation occurs during storage, re-heat to 45 °C while stirring on a heat-stir plate.

Starch solution

Potato starch pre-gelatinized in sodium phosphate buffer (1.0% w/v) is used as substrate. Pre-heat a heat-stir plate (setting it to 250 °C—300 °C is suggested) and pre-heat an incubator (or water bath) to 37 °C. Weigh 250 mg of potato starch into a heatproof bottle and add 750 μL of ethanol (80% v/v). Stir on a vortex mixer to wet all the starch powder (this is a critical step for the complete solubilisation of the starch). Add 20 mL of sodium phosphate buffer and mix again using a vortex mixer making sure that the powder is fully dispersed and there are no lumps in the solution. Cover the bottle with the lid to minimize evaporation (but making sure it is loose enough to let out excess steam) and place on the pre-heated heat-stir plate stirring at 180 rpm. When the solution starts bubbling, start the timer and boil on the heat-stir plate stirring continuously for exactly 15 min. Cool in the incubator/water bath for 15 min (or until it is safe to handle). Make up the volume of the starch solution to 25 mL in a volumetric flask by adding purified water. Store the solution in a closed bottle in an incubator (or water bath set to 37 °C) and use within 2 h. If the starch solution does not clarify significantly a new solution needs to be prepared, as this may indicate poor solubilisation and or gelatinization of the starch. Prepare a fresh solution each time as storing or freezing can cause starch retrogradation and influence the results of the assay.

α-amylase solutions

The preparation of the enzyme solutions is a critical step. Solutions prepared from enzyme powders should be carefully prepared following the same protocol each time to ensure adequate powder hydration and dispersion. After weighing the enzyme powder and adding the adequate amount of sodium phosphate buffer, stock solutions should be stirred in an ice bath (at around 250 rpm) for 20 min before any further dilutions (Graphical protocol in Fig. 6 and Picture S1 in the Supplementary Material). Subsequent dilution(s) of the stock solution(s) should be performed using sodium phosphate buffer to reach the recommended enzyme concentration of 1.0 ± 0.2 U/mL. For the four products tested in the ring trial, recommended concentrations are provided as reference in Table S7 (Supplementary material). For enzyme preparations, it is recommended to start from a stock solution prepared by adding 20 – 100 mg of enzyme powder to 25 mL of sodium phosphate buffer. For human saliva, a stock solution can be prepared by mixing 80 µL of saliva with 920 µL of buffer.

Each enzyme should be tested at three different concentrations prepared by diluting 0.65 mL, 1.00 and 1.50 mL of enzyme stock solution with 1.35, 1.00 and 0.50 mL of buffer, respectively (Table S3). These diluted enzyme solutions are referred to as solutions C1, C2 and C3. Enzyme solutions should always be kept on ice and used within 30 min of preparation.

Enzymatic assay

An overview of the enzyme assay is presented in (Fig. 6).

Preparative procedures

Before starting, the following preparations are recommended: set the heating-block (water bath) as required to ensure 37 °C inside the microtubes (see troubleshooting advice, Table 2); pre-warm the starch solution to 37 °C; prepare a polystyrene container with ice.

Sample collection tubes

For each incubation that will be carried out, label and pre-fill four microtubes with 75 μL of DNSA colour reagent.

Incubations

Set three microtubes (one for each diluted enzyme solution C1, C2 and C3) in the preheated thermal shaker and let the temperature equilibrate before adding 500 µL of pre-warmed potato starch solution to each tube (maintain the tubes closed until the enzyme is added to prevent evaporation). Add 500 µL of diluted enzyme solution C1, C2 and C3 to the corresponding tubes at regular intervals. It is recommended to start the timer immediately when the α-amylase solution is added to the first tube and leave a 30 s interval before each subsequent addition.

Sample collection

Take a 150 μL aliquot from each tube after 3, 6, 9 and 12 min of incubation (respecting the order and intervals at which the incubations were initiated) and transfer it immediately to the corresponding sample collection tube pre-filled with DNSA to stop the reaction. Each aliquot should be taken as closely as possible to its respective sample collection time, within a maximum of ± 5 s.

Absorbance measurements

Prepare the maltose calibrators by mixing 150 µL of each maltose calibrator with 75 µL of DNSA reagent. Centrifuge the samples and calibrators (1000 g, 2 min) so that all droplets are brought back into solution. Place the samples and calibrators in the thermal shaker (or boiling bath) (100 °C, 15 min) and then transfer them to an icebox to cool for 15 min. Add 675 µL of purified water to each tube and mix by inversion. Transfer the samples and calibrators to a cuvette or pipette to a microtiter plate (300 µL per well) and record the absorbance at 540 nm (A_540nm).

Ring trial organization

Preliminary testing

Throughout the protocol optimization phase, the assay was repeated multiple times by the coordinating laboratory to define practical aspects. Each of the four test products has been assayed at different concentrations. The final test concentrations were defined by choosing a test concentration that allowed for an adequate distribution of the endpoint measure (spectrophotometry absorbance) and communicated to the participating laboratories.

Protocol transference

A detailed written protocol (Supplementary material) was transferred to each participating laboratory including the recommendations for concentrations of the test products. All laboratories were invited to an online training session that included a video of the assay followed by a Q&A session to clarify any doubts. All labs carried out the assay and reported their results on a standard Excel file between May and November 2023.

Incubation temperatures

All laboratories tested the four enzyme preparations at 37 °C as described above. A subgroup of five laboratories also repeated the assays at 20 °C with the purpose of trying to establish a correlation between the results obtained at both temperatures.

For incubations at 20 °C protocol adaptations were performed as follows. A different recipe was used to prepare the 200 mM sodium phosphate buffer stock solution. It consisted of 1.26 g NaH₂PO₄, 1.29 g Na₂HPO₄ and 0.39 g NaCl. The dilutions (10 mL stock diluted to 95 mL with purified water) and pH (6.9 ± 0.3) were the same as those for the buffer used at 37 °C. All reagents and solutions requiring the use of buffer were freshly prepared using this buffer recipe. The recommended concentrations of the α-amylase stock solutions were adjusted to ensure that enough enzymatic activity was present.

Calculations

Calibration curve

The A_540nm of the colour reagent blank was subtracted from the readings of all maltose calibrators and their concentration (mg/mL) was plotted against the corresponding ΔA_540nm. For reference purposes, using a 96 well plate, the absorbance at 540 nm should increase linearly from approximately 0.05 (for the colour reagent blank) to 1.5 for the highest maltose concentration. The calibration blank should not be included as a data point in the calibration curve.

Enzyme activity definition

The definition of α-amylase activity resulting from the application of the newly developed protocol is the following:

• Based on the definition originally proposed by Bernfeld: one unit liberates 1.0 mg of maltose equivalents from potato starch in 3 min at pH 6.9 at 37 °C.

• Based on the international enzyme unit definition standards: one unit liberates 1.0 μmol of maltose equivalents from potato starch in 1 min at pH 6.9 at 37 °C.

Amylase activity units based on the definition originally proposed by Bernfeld were multiplied by the conversion factor 0.97 to convert the result into IU.

Enzyme activity calculation

The first step was to subtract A_540nm of the colour reagent blank from all readings. The calibration curve was then used to calculate the maltose concentrations (mg/mL) reached with each diluted enzyme solution (C1, C2 and C3) at each sampling point during incubations. Enzyme concentrations during incubations were then calculated as mg/mL for enzyme powders, or µL/mL for liquid (saliva) samples.

For each diluted enzyme solution (C1, C2 or C3), maltose concentrations (mg/mL) were plotted against time (t_min) and the corresponding linear regression was established to determine the reaction kinetics’ slope ((text{m}t{text{min}})). For each enzyme concentration, units of enzyme were calculated using the following equation.

A template Excel file is provided for calculations in the Supplementary Material.

Statistical analysis and assessment of method’s performance

Data visualization and statistical analyses have been performed in R (version 4.3.2)²⁹. The packages ggplot2³⁰ and ggdist³¹ have been used in the preparation of the plots presented in the manuscript.

Outlier analysis was conducted on non-transformed data to preserve the original variability and scale of the datasets. First, Cochran’s test (outliers package in R³²) was used to assess intralaboratory variability and did not reveal any outliers. Subsequently, for interlaboratory comparisons, boxplot analysis, Bias Z-scores and Grubbs’ test³² were employed complementarily. The results reported by one lab for three test products (pancreatin, α-amylase M and α-amylase S) assayed at 37 °C were more than 1.5 interquartile ranges below the 25^th or above the 75^th percentiles, consistent with unsatisfactory Bias Z-scores (|z|> 3). Grubb’s test confirmed these as outliers and they have been excluded from the statistical analysis. All results in the 20 °C dataset fell within 1.5 interquartile ranges of the 25^th and 75^th percentiles (Fig. 5), consistent with satisfactory Bias Z-scores (|z|< 2) (Supplementary Figure S4). While Grubbs’ test identified two potential outliers (Lab A for pancreatin and Lab D for α-amylase M), this outcome was considered less reliable due to the small sample size (n = 5) and lack of corroboration from boxplot and Bias Z-score analyses, and so these results were retained.

Statistical analysis of the dataset resulting from the implementation of the protocol at 37 °C has been carried out to investigate the effects of the tested products, concentrations and incubation conditions (thermal shaker vs. water bath with or without shaking) as well as the two-way and three-way interactions between these factors. Normality of this dataset has been confirmed through the Shapiro–Wilk test (p > 0.05). The homogeneity of variances, as assessed using Levene’s test in the Rstatix package version 0.7.2³³, was not confirmed (p < 0.001). Due to the limited availability of suitable non-parametric alternatives, a logarithm transformation was performed on this data set enabling homogenisation of the variances and application of a three-way ANOVA (Rstatix package). Statistically significant effects were further examined using Pairwise T-Test comparisons, applying Bonferroni adjustments for multiple comparisons as required. The results obtained when implementing the protocol at 20 °C were normally distributed, but homogeneity of variances was not confirmed for this dataset either. The corresponding logarithm transformed data frame did not conform to normality, hence the Kruskal–Wallis test was applied to examine the significance of the differences between the four products, followed by the Bonferroni-corrected Wilcoxon test for pairwise comparisons (all tests performed using the Rstatix package). Statistically significant effects have been accepted at the 95% level.

For each laboratory and product, an individual ratio of α-amylase activity at 37 °C to 20 °C was calculated, and the mean of these ratios across all laboratories was determined for each product. The 95% confidence interval for this mean ratio was computed using the t-distribution. Normal distribution and homogeneity of variances have been confirmed for this dataset, hence one-way ANOVA was used to investigate whether the ratios obtained for each product were significantly different.

For a thorough understanding of the method’s reliability, precision, and transferability across different laboratory settings three complementary metrics have been used: Z-scores based on bias scores for a standardized evaluation of systematic errors, repeatability and reproducibility.

Z-scores were calculated to standardize the comparison of bias scores across laboratories and products enabling to assess the overall agreement between individual laboratory results and the mean for each product. For each product, bias scores were first calculated for each laboratory using the mean of all laboratories as the reference value and then converted to z-scores:

x is the individual laboratory result, X is the mean of all laboratories, and SD is the standard deviation. Z-scores interpretation followed standard criteria with |z|≤ 2 as satisfactory, 2 <|z|< 3 as questionable, and |z|≥ 3 as potentially unsatisfactory.

Repeatability (measured as intralaboratory coefficient of variation, CV_r), which quantifies method precision within each laboratory, reflecting consistency under identical conditions, was calculated as the root mean square of the individual laboratory’s CVs:

CV_r is the coefficient of variation under repeatability conditions (intralaboratory); (i) indexes each laboratory, ({CV}_{i}) is the coefficient of variation for laboratory (i); L is the number of participating laboratories.

Reproducibility (measured as coefficient of variation, CV_R), a measurement of method’s consistency across different laboratories indicates its robustness to varying environments and operators, was calculated for each tested product as:

CV_R is the coefficient of variation under reproducibility conditions (interlaboratory); SD and X correspond to the standard deviation and mean values calculated from interlaboratory data.

Coefficients of variation below 30%^15,16 are frequently considered to be indicators of small intra- and interlaboratory variability. In some cases, critical thresholds for repeatability (intralaboratory CV) are set at 20%³⁴.

Every time a driver puts 10 litres of fuel in their car, they’re paying about $5 in tax that goes to the federal government.

That is, of course, unless they drive an electric vehicle. No petrol or diesel being bought means the government loses that 51c per litre.

Over recent years, the Productivity Commission has been calling for reforms on how revenue is raised from vehicles, and the Albanese government has been making noises that a road user charge could be put on EVs.

After the government’s economic reform roundtable this week, the treasurer, Jim Chalmers, said there was “a lot of conceptual support for road user charging” but said the details – including the type of charge and what vehicles would be included – were still to be determined..

State treasurers will discuss the concept when they meet next month.

According to the Electric Vehicle Council, about 298,000 battery electric vehicles and 81,000 plug-in hybrids have been sold in Australia so far. While that number is going up – EV sales were 13% of new car sales in the last quarter, the highest proportion on record – they still make up less than 2% of the 21.7m vehicles on the road.

“We’re still in the early adopter phase on EVs and well behind the adoption rates of other advanced economies,” says Prof Matt Burke, a transport expert at Griffith University and an EV owner.

He says there is “widespread agreement” and a “coalition of the willing” among policy experts and automobile clubs that a road user charge is coming.

But what it looks like is still up for debate. And some are worried that bringing a charge in too early could stymie the uptake of EVs.

Burke says governments could decide on a flat fee for EV users, or a fee related to how far vehicles drive that could also include an allowance for the weight of the vehicle.

“Electric vehicles don’t pollute in the same way as other vehicles, but they are a little bit heavier and that deteriorates the road surface a bit more,” he says.

Where the excise goes

But what is the fuel excise actually used for?

“People think that fuel excise pays for roads, but it doesn’t,” says Alison Reeve, director of the energy and climate program at the Grattan Institute.

According to the Parliamentary Budget Office, less than 6% of the fuel excise the government collects goes into a special account for states and territories to spend on road infrastructure.

Fuel excise is not what is known as a “hypothecated tax” – that is, a tax where the spending of the revenue is directed at a particular issue, like roads.

“[Fuel excise] hasn’t been hypothecated since 1992. But people still think it’s how it works,” says Reeve. “Some of that revenue might go to roads and some of it might go for new carpets in Parliament House.”

Falling revenue

The government’s revenue from fuel excise is definitely falling, but it’s not really because of EVs.

From a peak in the 1980s, the amount of fuel excise as a percentage of government revenue has fallen from about 11% to 4%, thanks largely to improved fuel efficiency.

The average passenger vehicle in Australia burned 11.3 litres of fuel per 100 kilometres in 2005. Now it burns only 6.9 litres, and with new efficiency standards in place for new vehicles, that number is likely to drop further.

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A future dominated by electric cars would see a dramatic drop in the fuel excise. Right now, the government is left with about $15bn from the tax after it has given full rebates to fuel that was burned off roads – mostly on mining sites – and partial rebates for heavy vehicles.

There would be good arguments that the amount of tax collected for driving a vehicle should reflect its cost to society, such as the health impacts of cars that run on fossil fuels and the thousands of premature deaths each year caused by breathing in particulates. There’s also the accumulating damage to the climate from the release of greenhouse gases.

But Reeve says the amount of excise charged by the government “is just a number that the government thinks it can get away with”.

Right policy, wrong time?

The Electric Vehicle Council’s head of legal, policy and advocacy, Aman Gaur, says a road user charge “is going to be a reality” but it needs to be paid by all vehicles, not just EVs.

The council represents car companies making and selling electric cars and says EV drivers should be exempt from a charge until 30% of new vehicle sales are electric.

“Just hitting EV drivers will be counterproductive,” says Gaur. “We support a charge for all types of vehicles. We don’t want to see a model that slams the breaks on our transition to a cleaner transport economy.”

The New South Wales government has proposed its own road user charge which, if there is no federal scheme, could be introduced in mid-2027. The proposed fee would be 2.97 cents per kilometre for an EV and 2.37c for a plug-in hybrid.

An EV driver doing 10,000km would expect to pay $300 under that proposed scheme. New cars sold in Australia average 6.9 litres of fuel use per 100km. A driver of an average new petrol car doing the same distance pays about $352 in fuel excise.

“Paying $300 might not be the end of the world for an EV enthusiast, but we need to think about the average person looking to buy an EV. For those people, that $300 might be the difference [of them buying the car],” Gaur says.

But there are other advantages to a general road user charge, Burke points out. It can give a government the ability to give discounts for some people, such as pensioners or the unemployed, or incorporate congestion charging.

A big question will be whether revenue from a road user charge would go into the government’s general coffers or if it would be ringfenced. The Australian Automobile Association has suggested revenue from road user charges on EVs should go into transport infrastructure, including building more recharging stations.

Helen Rowe, the transport lead at the Climateworks Centre, says: “If designed well, [a road user charge] could do far more than just plug a revenue gap.

“It could help cut congestion, reduce emissions, lower infrastructure costs and improve the overall efficiency of Australia’s transport network.”

The project commenced offshore construction following the final federal approval from BOEM last year. The project is 80% complete with all offshore foundations installed and 45 out of 65 wind turbines installed.

Ørsted is evaluating all options to resolve the matter expeditiously. This includes engagement with relevant permitting agencies for any necessary clarification or resolution as well as through potential legal proceedings, with the aim being to proceed with continued project construction towards COD in the second half of 2026.

Revolution Wind is fully permitted, having secured all required federal and state permits including its Construction and Operations Plan approval letter on 17 November 2023 following reviews that began more than nine years ago. Revolution Wind has 20-year power purchase agreements to deliver 400 MW of electricity to Rhode Island and 304 MW to Connecticut, enough to power over 350,000 homes across both states to meet their growing energy demand. As a reference, South Fork Wind, which is adjacent to Revolution Wind and uses the same turbine technology, delivered reliable energy to New York at a capacity factor of 53% for the first half of 2025, on par with the state’s baseload power sources.

Ørsted is investing into American energy generation, grid upgrades, port infrastructure, and a supply chain, including US shipbuilding and manufacturing extending to more than 40 states. Revolution Wind is already employing hundreds of local union workers supporting both on and offshore construction activities. Ørsted’s US offshore wind projects have totalled approximately 4 million labour union hours to date, 2 million of which are with Revolution Wind.

Ørsted is evaluating the potential financial implications of this development, considering a range of scenarios, including legal proceedings. Ørsted will, in due course, advise the market on the potential impact of the order on the plan announced on 11 August 2025 (company announcement 12/2025) to conduct a rights issue. Existing shareholders and prospective investors are advised to await further announcements by the company. 

Global Media Relations
Tom Christiansen
+45 99 55 60 17
tomlc@orsted.com

Investor Relations
Rasmus Keglberg Hærvig
+45 99 55 90 95
IR@orsted.com

About Ørsted
The Ørsted vision is a world that runs entirely on green energy. Ørsted develops, constructs, and operates offshore and onshore wind farms, solar farms, energy storage facilities, and bioenergy plants. Ørsted is recognised on the CDP Climate Change A List as a global leader on climate action and was the first energy company in the world to have its science-based net-zero emissions target validated by the Science Based Targets initiative (SBTi). Headquartered in Denmark, Ørsted employs approx. 8,300 people. Ørsted’s shares are listed on Nasdaq Copenhagen (Orsted). In 2024, the group’s revenue was DKK 71.0 billion (EUR 9.5 billion). Visit orsted.com or follow us.  

Attachments

Trump has repeatedly attacked the Fed’s chair, Jerome Powell, for not cutting its short-term interest rate, and even threatened to fire him. Powell, who will speak Friday at an economic symposium in Jackson Hole, Wyoming, says the Fed wants to see how the economy responds to Trump’s sweeping tariffs on imports, which Powell says could push up inflation.

Powell’s caution has infuriated Trump, who has demanded the Fed cut borrowing costs to spur the economy and reduce the interest rates the federal government pays on its debt. Trump has also accused Powell of mismanaging the U.S. central bank’s $2.5 billion building renovation project.

Firing the Fed chair or forcing out a governor would threaten the Fed’s venerated independence, which has long been supported by most economists and Wall Street investors. Here’s what to know about the Fed:

Why the Fed’s independence matters

The Fed wields extensive power over the U.S. economy. By cutting the short-term interest rate it controls — which it typically does when the economy falters — the Fed can make borrowing cheaper and encourage more spending, accelerating growth and hiring. When it raises the rate — which it does to cool the economy and combat inflation — it can weaken the economy and cause job losses.

Economists have long preferred independent central banks because they can more easily take unpopular steps to fight inflation, such as raise interest rates, which makes borrowing to buy a home, car, or appliance more expensive.

The importance of an independent Fed was cemented for most economists after the extended inflation spike of the 1970s and early 1980s. Former Fed Chair Arthur Burns has been widely blamed for allowing the painful inflation of that era to accelerate by succumbing to pressure from President Richard Nixon to keep rates low heading into the 1972 election. Nixon feared higher rates would cost him the election, which he won in a landslide.

Paul Volcker was eventually appointed chair of the Fed in 1979 by President Jimmy Carter, and he pushed the Fed’s short-term rate to the stunningly high level of nearly 20%. (It is currently 4.3%). The eye-popping rates triggered a sharp recession, pushed unemployment to nearly 11%, and spurred widespread protests.

Yet Volcker didn’t flinch. By the mid-1980s, inflation had fallen back into the low single digits. Volcker’s willingness to inflict pain on the economy to throttle inflation is seen by most economists as a key example of the value of an independent Fed.

Investors are watching closely

An effort to fire Powell would almost certainly cause stock prices to fall and bond yields to spike higher, pushing up interest rates on government debt and raising borrowing costs for mortgages, auto loans, and credit card debt. The interest rate on the 10-year Treasury is a benchmark for mortgage rates.

Most investors prefer an independent Fed, partly because it typically manages inflation better without being influenced by politics but also because its decisions are more predictable. Fed officials often publicly discuss how they would alter interest rate policies if economic conditions changed.

If the Fed was more swayed by politics, it would be harder for financial markets to anticipate — or understand — its decisions.

The Fed’s independence doesn’t mean it’s unaccountable

Fed chairs like Powell are appointed by the president to serve four-year terms, and have to be confirmed by the Senate. The president also appoints the six other members of the Fed’s governing board, who can serve staggered terms of up to 14 years.

Those appointments can allow a president over time to significantly alter the Fed’s policies. Former president Joe Biden appointed four of the current seven members: Powell, Cook, Philip Jefferson, and Michael Barr. A fifth Biden appointee, Adriana Kugler, stepped down unexpectedly on Aug. 1, about five months before the end of her term. Trump has already nominated his top economist, Stephen Miran, as a potential replacement, though he will require Senate approval. Cook’s term ends in 2038, so forcing her out would allow Trump to appoint a loyalist sooner.

President Donald Trump visits the Federal Reserve during renovations, July 24, 2025, in Washington. (AP Photo/Julia Demaree Nikhinson)

Trump will be able to replace Powell as Fed chair in May 2026, when Powell’s term expires. Yet 12 members of the Fed’s interest-rate setting committee have a vote on whether to raise or lower interest rates, so even replacing the Chair doesn’t guarantee that Fed policy will shift the way Trump wants.

Congress, meanwhile, can set the Fed’s goals through legislation. In 1977, for example, Congress gave the Fed a “dual mandate” to keep prices stable and seek maximum employment. The Fed defines stable prices as inflation at 2%.

The 1977 law also requires the Fed chair to testify before the House and Senate twice every year about the economy and interest rate policy.

Could the president fire Powell before his term ends?

The Supreme Court earlier this year suggested in a ruling on other independent agencies that a president can’t fire the chair of the Fed just because he doesn’t like the chair’s policy choices. But he may be able to remove him “for cause,” typically interpreted to mean some kind of wrongdoing or negligence.

It’s a likely reason the Trump administration has zeroed in on the building renovation, in hopes it could provide a “for cause” pretext. Still, Powell would likely fight any attempt to remove him, and the case could wind up at the Supreme Court.

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Meta will license technology from artificial intelligence image and video generation start-up Midjourney, as the social media group shifts towards working with third parties as its struggle to keep pace with rivals.

Alexandr Wang, Meta’s new chief AI officer, said in a post on X on Friday that the company planned to license Midjourney’s “aesthetic technology for our future models and products, bringing beauty to billions” in a “technical collaboration” between their research teams.

“To ensure Meta is able to deliver the best possible products for people it will require taking an all-of-the-above approach,” he added. “This means world-class talent, ambitious compute road map, and working with the best players across the industry.”

The tie-up will allow Meta to develop and integrate multimedia AI generation features into its apps, as chief executive Mark Zuckerberg has indicated that he expects AI-generated content to become more prominent on the platform.

The move comes as Zuckerberg pours billions of dollars into developing “superintelligence” that surpasses human intelligence. In recent months he has aggressively poached top AI researchers from competitors, doubled down on his investment in AI infrastructure and acquired AI voice company Play AI. Meta also took a stake in data labelling group Scale AI.

This week, Meta announced it was restructuring its AI group — recently renamed Meta Superintelligence Lab — into four distinct teams, the fourth overhaul in six months as it has struggled to solidify its organisational structure.

The Midjourney partnership marks a shift by Meta away from building all of its AI models and products in house, after its existing ones began to lag rivals. 

In 2024 Meta rolled out an image generation tool called Imagine, which allows users to generate images from text prompts. Last October it shared a research paper on a movie generation model, Movie Gen, that will generate and edit videos based on text prompts. Meta promised to integrate it fully into Instagram in 2025. 

However, the integration has yet to happen and industry insiders say the model already appears antiquated compared with Google’s Veo 3 and OpenAI’s Sora models, which have been released to consumers. 

The social media company had also abandoned plans to publicly release its flagship Behemoth large language model, according to people familiar with the matter, focusing instead on building new models.

Meta had started using third-party models internally for tasks such as coding, according to multiple people familiar with the matter, as faith in its Llama models has waned.

San Francisco-based Midjourney, founded in 2021 by David Holz, has become one of the most popular image generation companies, despite its chief executive refusing venture capital and instead opting to self fund. In June, it released its video model V1, which allows users to generate a short video from an existing image.

Holz said in a post on X on Friday that “bringing sublime tools of creation and beauty to billions of people is squarely within our mission”, adding that Midjourney remained an “independent, community-backed research lab, with no investors”.