Citations

Abstract Unvetted scientific claims about vaccines can spread rapidly and undermine decades of progress in immunization and public trust. A recent non-peer-reviewed analysis, promoted at political hearings, in the documentary “An Inconvenient Study,” and on social media, has been used to suggest that vaccinated children experience higher rates of chronic illness than unvaccinated peers. Despite standard regression models, critical flaws - uncontrolled confounding, exposure misclassification, unequal follow-up, and outcome multiplicity, invalidate causal inference. An independent group of international experts critically appraises this analysis and contrast its claims with the consistent evidence confirming vaccine safety and effectiveness. We also show how poorly designed studies can distort public discourse, amplify misinformation, and fuel vaccine hesitancy. We outline actions to strengthen research integrity, such as transparent data sharing, labeling of non-peer-reviewed content, and evidence-based communication. Safeguarding public health demands that vaccine evidence be methodologically sound, reviewed, and interpreted by qualified experts.

Abstract Background Acute hypoxemic and hypercapnic respiratory failure are among the most common reasons for ICU admission and need for invasive mechanical ventilation. Noninvasive respiratory support (NIRS) strategies—including high-flow nasal cannula (HFNC), noninvasive ventilation (NIV), and continuous positive airway pressure (CPAP)—may prevent intubation, improve outcomes, and reduce ICU utilization. However, there is uncertainty regarding optimal patient and modality selection, resulting in variable implementation. There are no clinical practice guidelines comprehensively addressing the use of the different noninvasive respiratory support strategies across the spectrum of acute respiratory failure. Objective To update and develop new evidence-based clinical practice recommendations informing noninvasive respiratory support use, including HFNC, NIV and CPAP, in adults with acute respiratory failure. Methods A multidisciplinary panel used the GRADE approach to address four PICO questions related to the use of NIRS for hypoxemic and hypercapnic respiratory failure, preoxygenation for intubation, and post-extubation respiratory support. Recommendations were informed by several systematic reviews and network meta-analyses. Results The panel made a strong recommendation for HFNC and a conditional recommendation for NIV or CPAP for adults with acute hypoxemic respiratory failure with close monitoring for the need for escalation of respiratory support, based primarily on effects on need for intubation. For acute hypercapnic respiratory failure, the panel made a strong recommendation for NIV to reduce mortality and need for invasive mechanical ventilation, and a conditional recommendation for HFNC only in patients with less severe hypercapnia and with mild acidemia (eg, pH > 7.25), provided that close monitoring and prompt escalation to NIV are available. The panel made a strong recommendation for HFNC or NIV for preoxygenation prior to endotracheal intubation to prevent peri-intubation hypoxemia. The panel also issued a risk-based recommendation, suggesting HFNC for low-risk patients and NIV for high-risk patients to reduce the need for re-intubation following extubation after critical illness. Conclusions Noninvasive respiratory support strategies are effective in improving outcomes in a range of clinical scenarios. We provide evidence-based recommendations, which can be further informed by patient risk, institutional capacity, and interface tolerance.

Abstract Roux‐en‐Y gastric bypass (RYGB) surgery is a highly effective technique for treating obesity. However, it may result in nutritional deficiencies. Here we report the case of a 55‐year‐old woman presenting with chronic diarrhea lasting 2 years, accompanied by lower limb paresthesia, macrocytic anemia, leukopenia, and thrombocytopenia. Her medical history included hypothyroidism treated with levothyroxine, and RYGB surgery performed in 2014, with intermittent micronutrient supplementation. The patient developed copper and zinc deficiencies. To our knowledge, this is the first report of a patient presenting with both deficiencies simultaneously with pancytopenia 10 years after RYGB. This case underscores the need to verify adherence to existing preoperative screening recommendations involving preoperative micronutrient measurements to assess micronutrient deficiency risks and multidisciplinary postoperative follow‐up, the importance of monitoring micronutrient levels, close follow‐up of both symptomatic and asymptomatic patients and evaluating adherence to supplementation regimens.

Abstract Histopathological assessment has served as the gold standard for diagnosing Alzheimer’s disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10−11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10−10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10−201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10−8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10−8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10−7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.

ABSTRACT Pregnancy-associated acute kidney injury (PrAKI) remains a major contributor to maternal and foetal morbidity worldwide, with an estimated incidence of 40–100 per 10 000 pregnancies. PrAKI is substantially more prevalent in low- and middle-income countries. Unlike acute kidney injury episodes unrelated to pregnancy, PrAKI arises in a unique physiological context characterized by haemodynamic adaptation, immune tolerance, hormonal modulation, and the presence of the foetal–placental unit. These pregnancy-specific factors alter renal reserve, endothelial stability, and inflammatory responsiveness. The pathogenesis of PrAKI is multifactorial. Haemodynamic vasodilation and increased glomerular filtration rates during pregnancy reduce renal functional reserve, rendering the kidney vulnerable to hypovolemia and sepsis. Placental ischaemia drives the release of antiangiogenic factors, particularly soluble Fms-like tyrosine kinase-1 (sFlt-1) and soluble endoglin, causing systemic endothelial dysfunction and glomerular endotheliosis. Disruption of immune tolerance and complement regulation further contributes to microangiopathy and autoimmune-mediated renal injury. Structural urinary tract changes, metabolic stressors, and maternal comorbidities amplify susceptibility. Beyond the index event, PrAKI has lasting consequences. Maladaptive renal repair promotes chronic kidney disease and long-term cardiovascular risk, while foetal exposure to uremic toxins and placental dysfunction contribute to intrauterine growth restriction, prematurity, and reduced nephron mass with lifelong cardio-renal implications. Understanding the pathophysiology of PrAKI, particularly endothelial, immunologic, and placental interactions, has direct diagnostic and therapeutic implications, including the use of angiogenic biomarkers and targeted haemodynamic strategies. Future research must prioritize molecular predictors, global registries, and mechanistic studies to reduce intergenerational kidney and cardiovascular disease.

Abstract Background and Aims Primary sclerosing cholangitis (PSC) increases the risk of developing cholangiocarcinoma (CCA), which is the leading cause of premature death in these patients. Current diagnostic methods for early detection of either condition remain largely suboptimal. This study investigates serum metabolites as potential non-invasive diagnostic biomarkers. Methods This multicenter international study analyzed 459 serum samples from 13 international centers, including patients with PSC (n = 216), PSC who developed CCA during follow-up (PSC to CCA; n = 24), PSC with concomitant CCA (PSC-CCA; n = 88), ulcerative colitis (UC; n = 14), and healthy individuals (n = 79). Serum metabolomics was evaluated by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and the accuracy of the single candidate metabolite biomarkers was further assessed. Machine learning was used to generate the best diagnostic and predictive algorithms. Results Fifty-two serum metabolites were identified as markers of PSC, independent of age, biological sex, cirrhosis, UC, or ursodeoxycholic acid (UDCA) treatment. A diagnostic model based on only six lipids distinguished PSC patients from healthy controls with high accuracy (AUC 0.952 discovery, 0.918 validation), independently of the levels of transaminases or cholestasis markers (i.e., GGT, AP, bilirubin) in blood. Subgroup analyses were performed in PSC patients with and without inflammatory bowel disease (IBD), confirming the model’s consistent performance across both subgroups when compared with their respective control groups – UC and healthy controls (AUC 0.959 and 0.939, respectively). The PSC model also confirmed its diagnostic performance in patients with established PSC-CCA or progression to CCA within 2 years, when compared to UC and healthy controls, showing AUC values ranging from 0.911 to 0.976. Additionally, 20 metabolites were significantly altered in PSC patients with CCA (PSC-CCA), regardless of demographics, cirrhosis, UDCA treatment, IBD status, or CCA subtype, and independently of serum transaminases and cholestatic markers levels. A model incorporating six lipids accurately identified PSC-CCA cases compared to PSC patients (AUC = 0.912 discovery; 0.896 validation), showing excellent performance in early-stage tumor detection (AUC 0.921) and outperforming CA19-9 (AUC 0.708). Importantly, the model retained high accuracy even in CCA patients with low CA19-9 levels (AUC 0.905) and in PSC patients who developed CCA within a year of clinical diagnosis (AUC 0.792). Conclusion The PSC and PSC-CCA blood tests provide a non-invasive approach for diagnosing PSC and PSC-CCA, addressing an important need in this population. Their integration into clinical practice could enhance risk stratification, early detection, personalized surveillance, and treatment decision-making in PSC patients.

Abstract Background and Aims Cholangiocarcinoma (CCA) is a rare and aggressive malignancy originating from the bile ducts, characterized by a poor prognosis and limited treatment options. Potentially curative interventions include surgical resection with negative margins (R0) and liver transplantation. However, despite these treatments, the likelihood of post-surgical recurrence remains high, with 50–70% of patients experiencing disease recurrence within five years. This highlights the urgent need for reliable prognostic biomarkers to guide clinical decision-making and personalize postoperative care. Numerous prognostic biomarkers identified in tumor tissue for CCA have been proposed, but many studies suffer from limitations such as single-center designs, small patient cohorts, and semi-quantitative techniques such as immunohistochemistry. These methods often rely on observer interpretation and antibody specificity, which can compromise objectivity and reproducibility. To overcome these limitations, we aimed to identify and validate robust quantitative prognostic biomarkers for CCA by analyzing tumor mRNA expression, integrating them using machine learning (ML), and developing a web-based tool to predict tumor recurrence and overall survival (OS). Method A systematic literature review was conducted to identify candidate prognostic tissue biomarkers for CCA, which were further validated using Cox proportional hazards (PH) OS analyses in five independent transcriptomic cohorts. An international multicenter cohort of 221 patients undergoing tumor resection was assembled, and tumor mRNA expression was analyzed using high-throughput TaqMan OpenArray RT-qPCR. Associations between clinical and molecular features and recurrence-free survival (RFS) and OS were assessed using Cox PH analyses. Six ML survival models were trained and evaluated using nested cross-validation, followed by feature reduction. Shapley Additive exPlanations (SHAP) values were applied to assess feature importance and enhance model interpretability. Results Among 496 candidate CCA prognostic biomarkers identified from the literature, 52 showed prognostic value in Cox PH analyses across transcriptomic cohorts. In the multicenter validation cohort, integrative Cox PH analyses incorporating clinical variables and gene expression identified 11 genes significantly associated with RFS and 25 with OS. The 11 RFS-associated genes, combined with five clinical variables, were used to train six ML survival models, among which the random survival forest (RSF) model demonstrated the highest predictive accuracy. Following training of RSF models across all combinations of four genes and five clinical variables, and subsequent feature importance–based selection, a final model incorporating three genes (FSCN1, LDHA, and SLC2A1) and four clinical variables (CCA subtype, resection margin status, biological sex, and age at surgery) achieved a testing time-dependent AUC of 0.82. This model significantly stratified patients into high- and low recurrence-risk groups, with a median RFS (mRFS) of 8 months and 70 months, respectively. In addition, an OS prediction model using the same feature set achieved a testing AUC of 0.775 and significantly stratified patients according to 5-year OS risk, with a median OS (mOS) of 23 months in the high-risk group and 77 months in the low-risk group. Conclusion We identified and validated a robust panel of prognostic tissue biomarkers in patients with resected CCA. Integration of these biomarkers with clinical variables into ML-based prognostic models enabled accurate prediction of RFS and OS. The resulting web-based tool allows clinicians to input RT-qPCR results for the validated biomarkers and obtain precise, individualized prognostic predictions for postoperative recurrence and survival.