Explore the vast range of titles available.

Background Although observational epidemiological studies have found that smoking is positively associated with risk of rheumatoid arthritis (RA), assessing the causality of this relationship has remained elusive because conventional observational studies are susceptible to bias such as confounding and reverse causation. Here, we applied the Mendelian randomization (MR) approach to examine the potential causal relationship between smoking and risk of RA. Methods Summary statistics data for RA were obtained from a meta-analysis of genome-wide association studies (GWAS), including 14,361 RA cases and 43,923 controls of European ancestry. The instrumental variables (IV) and the genetic association estimates for smoking initiation and lifetime smoking were obtained from a GWAS meta-analysis including 1,232,091 individuals and a GWAS of 462,690 individuals of European ancestry, respectively. MR analyses were performed using the inverse-variance weighted (IVW) method and supplemented with the weighted-median method. Potential pleiotropy was assessed using the MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test and MR-Egger regression. Sensitivity analyses were further performed to test the robustness of the association. Results We found that compared with never smokers, genetic predisposition to smoking initiation was positively associated with risk of RA (odds ratio (OR) = 1.32, 95% confidence interval (CI) = 1.15–1.52, P  = 9.17 × 10 −5 using the IVW method). Similarly, genetically predicted lifetime smoking was associated with an increased risk of RA (OR = 1.55, 95% CI = 1.13–2.14, P  = 0.007). Sensitivity analyses using alternative MR methods and different sets of IVs produced similar results, suggesting the robustness of our findings. Conclusions These results provide support for a causal association between smoking and increased risk of RA. Further studies are warranted to explain the underlying mechanisms of smoking in the development of RA.

Purpose: Colorectal cancer (CRC) is the third-most frequently diagnosed cancer globally. Studies have linked low serum albumin with increased risk of CRC, but the causal nature of the association remains unclear. In the present study, we explored the potential causal relationship using bidirectional Mendelian randomization (MR). Methods: Instrumental variants for albumin were obtained from a genome-wide association study (GWAS) on 102,223 Eastern Asian participants to investigate the effect of albumin on CRC. Summary statistics of CRC were obtained from a GWAS on 7,062 CRC cases and 195,745 controls of Eastern Asian ancestry. Bidirectional MR analysis was performed using inverse variance weighting (IVW) for primary analysis, supplemented with a maximum likelihood-based method, MR-PRESSO test, leave-one-out analysis, and MR-Egger regression. Stratification analyses were further performed. Results: We found that genetically predicted serum albumin per unit was associated with a lower risk of CRC (OR 0.75, 95% CI 0.59-0.95 with IVW). No evidence of pleiotropy was observed. Sex-stratified MR analysis showed that serum albumin was inversely associated with risk of CRC in men (OR 0.71, 95% CI 0.53-0.96), but not in women (OR 0.81, 95% CI 0.55-1.19) using IVW. Reverse MR analysis suggested a genetic predisposition toward CRC was not associated with serum albumin. Conclusion: Our study revealed a suggestive sex disparity in the effect of albumin, which deserves further exploration of the potential biological mechanism. Keywords: albumin, colorectal cancer, Mendelian randomization, single-nucleotide polymorphism, genome-wide association study, instrumental variables

Numerous studies have indicated that smokers have an increased risk of developing ischemic stroke. However, less is known about the causal relationship between cigarette smoking and ischemic stroke subtypes. In the present study, we aim to determine whether genetically predicted cigarette smoking was associated with subtypes of ischemic stroke using Mendelian randomization (MR). We used summary-level genetic association data from the MEGASTROKE consortium, including 438,847 individuals of European ancestry (34,217 cases of ischemic stroke and 404,630 controls). We used 176 single nucleotide polymorphisms as instrumental variables, which were previously identified to be associated with smoking in the Study of the Social Science Genetic Association Consortium (n=518,633). MR analyses were performed using inverse-variance-weighted method, weighted-median method, and MR-Egger regression. We found that genetically predicted smoking was associated with a higher risk of ischemic stroke (odds ratio (OR): 1.24, 95% CI: 1.10-1.39) and large artery ischemic stroke (OR: 1.52, 95% CI: 1.14-2.02), but not with risk of cardioembolic ischemic stroke or small vessel ischemic stroke. Sensitivity analyses using alternative MR approaches produced similar results. Genetic predisposition toward smoking is causally associated with a higher incidence of large artery ischemic stroke. Further work is warranted to clarify the underlying mechanism of smoking in the development of large artery ischemic stroke.

Sleep disturbance is an important factor in the pathophysiology and progression of psychiatric disorders, but whether it is a cause, or a downstream effect is still not clear. To investigate causal relationships between three sleep-associated traits and seven psychiatric diseases, we used genetic variants related to insomnia, chronotype and sleep duration to perform a two-sample bidirectional Mendelian randomisation analysis. Summary-level data on psychiatric disorders were extracted from the Psychiatric Genomics Consortium. Effect estimates were obtained by using the inverse-variance-weighted (IVW), weights modified IVW, weighted-median methods, MR-Egger regression, MR pleiotropy residual sum and outlier (MR-PRESSO) test and Robust Adjusted Profile Score (RAPS). The causal odds ratio (OR) estimate of genetically determined insomnia was 1.33 (95% confidence interval (CI) 1.22-1.45; p = 5.03 × 10-11) for attention-deficit/hyperactivity disorder (ADHD), 1.31 (95% CI 1.25-1.37; p = 6.88 × 10-31) for major depressive disorder (MDD) and 1.32 (95% CI 1.23-1.40; p = 1.42 × 10-16) for post-traumatic stress disorder (PTSD). There were suggestive inverse associations of morningness chronotype with risk of MDD and schizophrenia (SCZ). Genetically predicted sleep duration was also nominally associated with the risk of bipolar disorder (BD). Conversely, PTSD and MDD were associated with an increased risk of insomnia (OR = 1.06, 95% CI 1.03-1.10, p = 7.85 × 10-4 for PTSD; OR = 1.37, 95% CI 1.14-1.64; p = 0.001 for MDD). A suggestive inverse association of ADHD and MDD with sleep duration was also observed. Our findings provide evidence of potential causal relationships between sleep disturbance and psychiatric disorders. This suggests that abnormal sleep patterns may serve as markers for psychiatric disorders and offer opportunities for prevention and management in psychiatric disorders.

Coptotermes formosanus Shiraki is a well-known wood-feeding termite, which can degrade not only cellulose and hemicellulose polysaccharides, but also some aromatic lignin polymers with its enzyme complex to the woody biomass. In this study, a very abundant protein was discovered and purified, using a three-step column chromatography procedure, from the tissue homogenate of the salivary glands and the gut of C. formosanus. Mass spectrometric analysis and the following peptide searching against the mRNA database toward this termite species indicated that the novel protein was a hemocyanin enzyme, termed as Hemo1, which further exhibited a strong oxidase activity in the substrate bioassays toward ABTS [2,2′-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)], as well as other aromatic analogues, such as catechol and veratryl alcohols. This oxidative protein was an acid-favored enzyme with a molecular weight at 82 kDa, and highly active at 80 °C. These findings indicated that the novel protein, hemocyanin, discovered from the gut system of C. formosanus, might be an important ligninolytic enzyme involved in the biomass pretreatment processing, which will potentially enhance the digestibility and utilization of biomass polysaccharides in termite digestive systems.

Clostridium thermocellum is an anaerobic, thermophilic, cellulolytic, and ethanogenic bacterium. It produces an extracellular multiprotein complex termed the cellulosome, which consists of >70 subunits, most of them glycosyl hydrolases. It also produces many free glycosyl hydrolases. How the organism commands such a large number of genes and proteins for biomass degradation is an intriguing yet unresolved question. We identified glyR3, which is cotranscribed with the cellulase/hemicellulase genes celC and licA, as a potential cellulase transcription regulator. The gel-shift assay (EMSA) revealed that the recombinant GlyR3 bound specifically to the celC promoter region. GlyR3 was also identified from the lysate of the lichenan-grown cells, which bound to the same sequence. DNase I footprinting and competitive EMSA showed the binding site to be an 18-bp palindromic sequence with one mismatch. The DNA-binding activity was specifically inhibited by laminaribiose, a β-1-3 linked glucose dimer, in a dose-dependent manner. In in vitro transcription analysis, celC expression was repressed by rGlyR3 in a dose-dependent manner. The repression was relieved by laminaribiose, also in a dose-dependent manner. These results indicate that GlyR3 is a negative regulator of the celC operon consisting of celC, glyR3, and licA, and inducible by laminaribiose. Thus, the bacterium may modulate the biosynthesis of its enzyme components to optimize its activity on an available biomass substrate, in this case, β-1-3 glucan, because both CelC and LicA are active on the substrate. The results further indicate that, despite the insolubility of the biomass substrate, regulation of the degradative enzymes can be accomplished through soluble sugars generated by the action of the enzymes.

Clostridium thermocellum, an anaerobic, thermophilic, and ethanogenic bacterium produces a large cellulase complex termed the cellulosome and many free glycosyl hydrolases. Most cellulase genes scatter around the genome. We mapped the transcripts of the six-gene cluster celC–glyR3–licA–orf4–manB–celT and determined their transcription initiation sites by primer extension. Northern blot showed that celC–glyR3–licA were co-transcribed into a polycistronic messenger with the transcription initiation site at −20 bp. Furthermore, RT-PCR mapping showed that manB and celT, two cellulosomal genes immediately downstream, were co-transcribed into a bicistronic messenger with the initiation site at −233 bp. In contrast, rf4 was transcribed alone with the two initiation sites at −130 and −138 bp, respectively. Finally, quantitative RT-PCR analysis showed that celC, glyR3, and licA were coordinately induced by growing on laminarin, a β-1,3 glucan. Gene expression peaked at the late exponential phase. Taking together with our previous report that GlyR3 binds to the celC promoter in the absence of laminaribiose, a β-1,3 glucose dimer, these results indicate that celC, glyR3, and licA form an operon repressible by GlyR3 and inducible by laminaribiose, signaling the availability of β-1,3 glucan. The celC operon is the first glycosyl hydrolase operon reported in this bacterium.

Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

Enzymatic cellulose degradation is a heterogeneous reaction requiring binding of soluble cellulase molecules to the solid substrate. Based on our studies of the cellulase complex of Clostridium thermocellum (the cellulosome), we have previously proposed that such binding can be brought about by a special \"anchorage subunit.\" In this \"anchor-enzyme\" model, CipA (a major subunit of the cellulosome) enhances the activity of CelS (the most abundant catalytic subunit of the cellulosome) by anchoring it to the cellulose surface. We have subsequently reported that CelS contains a conserved duplicated sequence at its C terminus and that CipA contains nine repeated sequences with a cellulose binding domain (CBD) in between the second and third repeats. In this work, we reexamined the anchor-enzyme mechanism by using recombinant CelS (rCelS) and various CipA domains, CBD, R3 (the repeat next to CBD), and CBD/R3, expressed in Escherichia coli. As analyzed by nondenaturing gel electrophoresis, rCelS, through its conserved duplicated sequence, formed a stable complex with R3 or CBD/R3 but not with CBD. Although R3 or CBD alone did not affect the binding of rCelS to cellulose, such binding was dependent on CBD/R3, indicating the anchorage role of CBD/R3. Such anchorage apparently increased the rCelS activity toward crystalline cellulose. These results substantiate the proposed anchor-enzyme model and the expected roles of individual CipA domains and the conserved duplicated sequence of CelS.

Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a 1,100-task benchmark of primary care-to-specialist consultation cases to measure the frequency and severity of potentially harmful errors from LLM-generated medical consultation recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 20 notable LLMs and 4 widely used retrieval-augmented generation (RAG) clinical AI tools, direct application of recommendations carried potential for severe harm in up to 24.6% of cases, with errors of omission accounting for more than 80% of severe errors. Harm potential was not uniform across systems, with clinical AI tools outperforming generalist LLMs, and multi-agent AI teaming further improving performance in generalist models. In a randomized study of 101 U.S.-licensed generalist physicians, AI assistance improved physician performance compared to conventional resources. However, AI-assisted physicians frequently omitted valuable AI-generated recommendations and still scored lower than many AI systems alone. Had those recommendations been incorporated, combined human-AI responses would have outperformed both the human and AI system as used, suggesting complementary strengths and unrealized potential in human-AI teaming. Collectively, these results show that despite strong performance on medical knowledge benchmarks, widely used AI tools can produce medical consultation advice with the potential for severe harm, and highlight the need for explicit measurement of clinical safety. The benchmark and leaderboard are publicly available to support ongoing evaluation and improvement of AI systems used for clinical care.