Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Nanotechnology
DFT and machine learning investigation of Au/Pt-decorated SnS2 monolayers for asthma and COPD diagnosis
Asthma and chronic obstructive pulmonary disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-decorated SnS2heterostructure for exhaled NO2detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO2detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While two-dimensional (2D) SnS2offers strong potential as a sensing platform, prior studies relied mainly on density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and machine learning (ML) to investigate the gas sensing performance of pristine and Au/Pt-decorated SnS2monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS2showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO2, among which XGBoost achievingR2= 0.9961. Both ML and DFT methods consistently identified NO2as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D material-based sensing devices.
24 July 2026
Read appraisal →JMIR mental health
Large Language Models and Their Applications in Mental Health: Scoping Review
BACKGROUND: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. OBJECTIVE: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. METHODS: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. RESULTS: In total, 41 papers were selected. Many studies included evaluations on OpenAI's GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. CONCLUSIONS: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
16 May 2026
Read appraisal →Experimental eye research
Automated segmentation of pterygium lesions using multiscale deep learning networks
Pterygium is an eye condition that needs to be identified at an early stage so that its progression can be mitigated in order to avoid the possible threat of visual impairment. One of the important low-level modules in determining the severity level of the pterygium automatically is to measure the extent of fibrovascular tissue encroachment onto the corneal and pupil regions. Therefore, it is important to develop a semantic segmentation method to map and quantify the lesion tissues accurately, especially through the deep learning technique. As the pterygium condition progresses from the trace stage to the severe stage, the lesions will generally become bigger but maintain more or less the same shape, which looks like a wedge. Taking inspiration from this unique pattern of pterygium lesions, this work aims to explore multiscale deep learning networks to capture the variable scales of the lesion features effectively. All the proposed multiscale modules are embedded at the bottleneck layer of the base UNet architecture, whereby the feature map size is the smallest to reduce the potential increment in the computational burden. Apart from that, the total number of convolutional channels is fixed to 1024, whereby the multiscale module replaces the first convolutional set at the UNet's bottleneck layer. Two main multiscale modules have been explored, which are spatial pyramid pooling (SPP) and atrous spatial pyramid pooling (ASPP) modules. Furthermore, each of these modules has been expanded by analyzing the equal-flow (EF) and waterfall-flow (WF) patterns in constructing the parallel paths that represent the multiscale features. According to the simulation results, the best segmentation network is UNet, which has been embedded with three parallel paths of the EF-ASPP module that produces the lowest Hausdorff distance of 16.75 pixels. As a result, the severity level of pterygium can be better predicted through accurate extraction of the lesion map. This work can be further improved by analyzing different network architectures of the multiscale module, especially networks that focuses on the larger feature maps size, but surely with a trade-off in computational burden.
5 May 2026
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