Browse Articles

Phylogenetic based dissection of eukaryotic Mo-insertase functionality: From mechanism to complex assembly

Molybdenum cofactor (Moco) biosynthesis is vitally important for all organisms, yet the domain organization of the eukaryotic molybdenum insertase (Mo-insertase) remains enigmatic. We combine extensive phylogenetic reconstructions, sequence analysis and structural modeling in order to uncover evolutionary and functional principles of eukaryotic Mo-insertases. We note, that the vast majority of plant, fungi and animal species evolved fused E- and G-domains, yet the orientation of both domains in the fusion proteins differs among different eukaryotic lineages. Despite the divergent domain arrangements amongst eukaryotic Mo-insertases the E-domain active site is well conserved, with very few tolerated substitutions. Among the Mo-insertases from different eukaryotic species, vertebrate gephyrin is the only Mo-insertase with a dual function as – next to its metabolic function – it scaffolds inhibitory neurotransmitter receptors in the post synapsis. Gephyrin is surprisingly high conserved, including surface patches not directly involved in catalysis and receptor clustering. This profile suggests additional, as yet uncharacterized, functional constraints on gephyrins evolution. Together, our results reveal how eukaryotic Mo-insertases combine evolutionary domain organization plasticity with stringent active site conservation and recognize the evolutionary constraint on gephyrin’s surface conservation to be extreme, likely due to its mutual metabolic and neuronal function.

Alcohol consumption and tobacco smoking associated with decreased antiretroviral therapy adherence among people living with HIV in Zambia: Evidence from a 2023 national NCDs/ HIV survey

Background In order to achieve the 95-95-95 goals and obtain optimal benefits from ART, PLWH must adhere to prescribed medication. Aim Investigate factors associated with ART adherence in a clinical setting in Zambia. Methods National cross-sectional study involving 193 clinics across all 10 provinces of Zambia. The primary outcomes assessed were ART adherence status, associated behavioral factors, and clinical characteristics. Logistic regression analysis was conducted to evaluate associations between these factors and ART adherence. Both unadjusted odds ratios (UOR) and adjusted odds ratios (AOR) were calculated, with adjustments made for relevant covariates, and all estimates reported with 95% confidence intervals. Results Of the 5,204 PLWH, 7.1% were non-adherent to ART (9.7% males vs 5.9% females). Of those who were non adherent, 60% and 22% consumed alcohol and smoked tobacco respectively. In adjusted analysis, age; 45–59 (AOR: 0.5, 95% CI:0.32–0.79), 60+ (AOR: 0.26, 95% CI: 0.12–0.59) and education; Primary (AOR: 0.6, 95% CI: 0.4–0.9), secondary (AOR: 0.57, 95% CI: 0.4–0.82), college/university (AOR: 0.5, 95% CI: 0.29–0.89) were associated with decreased odds of being non-adherence to ART. In contrast, being male (AOR: 1.45, 95% CI:1.07–1.98), being in informal employment (AOR:1.65, 95% CI:1.15–2.36), having average yearly income; > $160 (AOR: 1.55, 95% CI: 1.16–2.06), Alcohol consumption; healthy consumption (AOR: 2.72, 95% CI: 1.9–3.89), unhealthy consumption (AOR: 2.76, 95% CI: 1.8–4.22), indeterminant consumption (AOR: 3.03, 95% CI: 2.04–4.5) and tobacco smoking (AOR: 95% CI: 2.03 1.42–2.9) were associated with increased odds of being non-adherence to ART. Conclusions ART non-adherence among PLWH in Zambia is 7.1%, with higher rates in males. Substance use especially alcohol and tobacco are common among the non-adherent. The associations between alcohol consumption, tobacco smoking, and ART adherence highlight the potential value of targeted support strategies. These results can inform future longitudinal research and contribute to the development of evidence-based interventions

Retraction: Enhanced heart disease diagnosis and management: A multi-phase framework leveraging deep learning and personalized nutrition

Internet-based vestibular rehabilitation versus written instructions after acute vertigo: A randomised controlled trial

Background Acute vestibular syndrome (AVS) causes sudden and continuous vertigo, dizziness, and postural instability and is a common reason for emergency department visits, most commonly due to vestibular neuritis and, in rare cases, stroke. Vestibular rehabilitation (VR) is an evidence-based exercise therapy that facilitates vestibular compensation and is effective in chronic vestibular disorders. However, evidence for VR in acute vertigo remains limited and, despite guideline support, it remains underused in practice. Internet-based VR has demonstrated benefit in chronic dizziness but has not been evaluated in acute vertigo. This study aimed to evaluate the efficacy of an internet-based VR tool compared with standard care (written instructions) in reducing vestibular symptoms after acute onset vertigo. Methods and Findings A multicentre, randomised, evaluator-blinded superiority trial was conducted across nine hospitals in Sweden. Adults with ongoing AVS were recruited within 1–7 days after symptom onset and randomised (1:1) to six weeks of internet-based VR with personalised, progressively adjusted home exercises or to written instructions for home-based VR exercises. The primary outcome was the between-group difference in vestibular symptoms at six weeks measured using the Vertigo Symptom Scale Short Form (VSS-SF; range 0–60), with a difference of ≥3 points prespecified as clinically significant. Secondary outcomes included dizziness-related disability, walking speed, and balance. Of 184 randomised participants, 183 were included in the analyses (94 online VR; 89 written instructions; median age 56 years). In the intention-to-treat analysis, both groups improved at six weeks, with no significant between-group difference (adjusted mean difference −2.0 points, 95% CI −4.9 to 0.9; p = 0.18). Per-protocol analyses were consistent (−1.7 points, 95% CI −4.7 to 1.3; p = 0.27). Over 12 weeks, both groups showed reduced vestibular symptoms and improved disability, balance, and walking speed, without significant between-group differences. No serious adverse events were attributed to the interventions, and compliance was high in both groups. Conclusions Internet-based VR was not superior to written instructions in reducing vestibular symptoms six weeks after acute onset vertigo. Both groups demonstrated improvement from baseline, with no statistically significant between-group differences. These findings suggest that ensuring access to vestibular rehabilitation exercises may be more important than the specific mode of delivery after acute onset vertigo, and internet-based tools represent viable alternatives for patients who prefer or may benefit from a digital format. Trial registration Clinicaltrials.gov NCT05056324. https://clinicaltrials.gov/study/NCT05056324 . Registered on September 24, 2021.

Musculoskeletal surgeons use mixed reasoning rather than pure Bayesian strategies in clinical practice

Objectives To inform efforts to promote regular and normalized Bayesian reasoning, we studied factors associated with the degree to which surgeons use Bayesian reasoning to navigate uncertainty across different clinical scenarios. Methods Science of Variation Group members (153; 58% North America, 30% Europe, 69% over 15 years of experience) completed an online survey reading 8 scenarios of test and treatment decisions and chose one of 4 answer options with higher scores indicating more Bayesian reasoning. Internal consistency of the survey was assessed using Cronbach alpha. Results The average Bayesian reasoning score across all scenarios was 3.0 (IQR 2.7–3.2) on a 4-point scale, indicating a relative context-dependent variability. Completely non-Bayesian reasoning was selected least often (8.6%, 90 of 1,044) and fully Bayesian reasoning represented 29% (301 of 1,044) of responses. Most surgeons showed mixed patterns (defined as reasoning in which prior probability is acknowledged but underweighted, without explicit probabilistic updating): 85% (121 of 142) used fully Bayesian reasoning at least once (121 of 142) while 42% (60 of 142) used completely non-Bayesian reasoning at least once. The Cronbach alpha was 0.43 suggesting the scenarios measured different aspects of clinical reasoning rather a unified construct. Conclusions The finding that surgeons use relatively context-dependent reasoning suggests an opportunity for surgeons to develop and practice Bayesian reasoning strategies both in training programs and in practice.

Assessing spatio-temporal coupling between intangible cultural heritage and rural development in China (2013–2022) through coupling coordination analysis and random-forest prediction

Effect of spraying distance and arc current on the corrosion behavior of arc-sprayed Zn–15Al coatings for rolling stock components

IL-33 accelerates orthodontic tooth movement by promoting M1 differentiation of macrophages and osteoclast formation

Performance, safety, and limitations of multimodal large language models in wound image assessment

Microwave-induced modulation of intracellular distribution of peptides based on mitochondrial targeting sequences

Freedom of speech and civic participation predicts national commitments towards animal protection

A quantitative assessment of diagenetic controls in the lower Sarvak reservoir of an Iranian oil field, Zagros Basin

Bang! Exploding immune cells splatter potent toxins everywhere

Correction: The prevalence of mental health disorders and stress coping strategies among forced migrants from Ukraine and Russia

An intelligent drug supply chain management and recommendation framework using blockchain and TRPO-driven multi-agent learning

Carrier

Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation

Abstract Retinal lesion segmentation is one of the critical tasks to analyze retinal diseases. Many researchers have proposed deep-learning models to extract lesions from the retinal scans. However, these models often rely on image features that might not be clinically meaningful for identifying the retinal lesions. Additionally, these models need pixel-level ground truths, which are challenging to procure in the real world. To overcome these issues, we present a novel language-assisted multimodal convolutional transformer pipeline that aligns image features with the text features, where the text features are extracted from the prompts that contain clinically meaningful information about the retinal lesions, and the image features are generated from the retinal scans. This alignment between image and text features is established with one-time training using the proposed loss function. Afterward, the proposed network can robustly extract retinal lesions across different datasets at the inference stage. Moreover, since the proposed network infers learning from the text prompts, it does not require additional training rounds using pixel-level ground truth annotations to adapt to new datasets like the state-of-the-art methods. The proposed network is thoroughly tested on six public datasets, and it outperforms the state-of-the-art by achieving up to 7.77% improvements in terms of intersection-over-union.

Neovascularization of the Disk in Proliferative Diabetic Retinopathy

Design and optimization of a climate-resilient hybrid renewable microgrid for rural electrification in flood-affected regions

Abstract Seasonal flooding in the wetland regions of rural Bangladesh frequently disrupts national grid infrastructure, leaving hundreds of thousands of households without reliable electricity access for extended periods, a critical energy vulnerability that conventional grid extension strategies have consistently failed to resolve. To address this challenge, this study designs and optimizes a hybrid renewable energy microgrid combining solar photovoltaic (PV) panels, wind turbines (WT), and a battery energy storage system (BESS), connected to the existing grid via a power converter, to provide reliable and affordable electricity to 200 rural households in Mithamain Upazila, Kishoreganj District, Bangladesh. The system, modeled using HOMER Pro (version 3.14.2), achieves a competitive cost of energy of $0.02995/kWh, which is comparable to conventional rural electricity tariffs. The system’s net present cost is $107,712.60, with an initial investment of $48,107 and an annual operating cost of $895.93. Additionally, the system produces 73.9% of its energy from renewable sources, which reduces carbon dioxide emissions by 43,675 kg every year. Sensitivity analysis highlights the system’s strength in different weather and economic situations, confirming that hybrid microgrids are a feasible and resilient solution for energy access in rural areas that have been hit by floods. This research demonstrates that hybrid renewable energy microgrids are a long-term, low-cost solution for rural electrification in flood-prone areas.

Global Cholera-Control Efforts — Progress and Remaining Challenges