Median nerve cross-sectional area (CSA) is the best-validated sonographic metric for carpal tunnel syndrome, but an isolated cutoff cannot represent the full structural and clinical heterogeneity of entrapment neuropathy. This Perspective proposes a descriptive phenotyping framework in which the interpretation of sonographic findings depends on the clinical question and stage of care; the framework is intended to organize reporting and generate testable hypotheses, not to serve as a validated classification or treatment-allocation rule. At diagnosis, internal-reference ratios and dynamic nerve mobility complement inlet CSA by characterizing focal swelling and impaired gliding. Before intervention, ultrasound identifies anatomical variants and secondary lesions, maps transverse and longitudinal safe corridors, and supports real-time device tracking and technique-specific completion checks. After release, median nerve CSA generally decreases over weeks to months, but structural change correlates inconsistently with symptoms, and persistent enlargement alone does not establish treatment failure. Postoperative ultrasound is therefore most useful for symptom-directed problem-solving when incomplete release, perineural fibrosis, or recurrent compression is suspected; the available evidence does not support routine serial imaging during satisfactory recovery. Ultrasound should complement, rather than replace, clinical evaluation and electrodiagnostic testing by linking morphology, biomechanics, procedural anatomy, and symptom-directed postoperative assessment.
One of the diseases causing the greatest economic damage to the poultry industry is coccidiosis. Effective in vitro screening platforms can accelerate research on coccidiosis control by identifying and prioritizing interventions for subsequent validation in more resource- and labor-intensive in vivo studies. Consequently, increasing the invasion rate of Eimeria sporozoites is considered a crucial step in elucidating the process of parasitic infection and defense mechanisms. This study investigated the invasion rate of E. tenella sporozoites upon the supplementation of whole-cell lysates and quantified the sporozoite load using microscopic counting and an absolute quantitative real-time PCR assay targeting the internal transcribed spacer 1 (ITS-1) region of the nuclear ribosomal DNA of E. tenella. Compared to cell culture media with invasion rates of 13.9–34.4%, the whole-cell lysate supplementation significantly enhanced sporozoite invasion to 38.7–81.3% over time (p < 0.05). These findings enable the efficient use of the large quantities of sporozoites required for repeated experiments and also reduce the need for the difficult and time-consuming process of extracting sporozoites from sporulated oocysts. This result is expected to be highly beneficial for cell-based experiments utilizing Eimeria sporozoites.
The relationship between systemic inflammation and thyroid nodule size has received less attention than its role in malignancy. This retrospective study evaluated the associations of the neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte (LMR), monocyte-to-high-density lipoprotein cholesterol (MHR), and neutrophil-to-high-density lipoprotein cholesterol (NHR) ratios with maximum ultrasonographic thyroid nodule diameter. Of 279 screened records, 161 patients aged 18–70 years were analyzed. Spearman correlations with Holm correction constituted the primary analysis; multivariable linear regression and five machine-learning regression algorithms evaluated with repeated five-fold cross-validation (n = 155) were exploratory. In unadjusted analysis, LMR was the only index significantly correlated with nodule diameter (ρ = 0.230, 95% bootstrap CI: 0.069–0.384; Holm-adjusted p = 0.020). After adjustment for age, sex, body mass index, thyroid functional status, and HDL cholesterol, LMR remained associated with nodule diameter (β = 1.824 mm per unit; 95% CI: 0.171–3.478; n = 144), although the model explained little variance (R2 = 0.106). Random Forest achieved the lowest cross-validated root mean squared error (10.94 ± 1.55 mm; R2 = 0.116 ± 0.305), and LMR showed the highest permutation importance. These indices alone were insufficient for clinically reliable prediction of nodule diameter, and the weak LMR association should be regarded as hypothesis-generating.
The gut microbiome plays an important role in metabolic regulation, but the role of its viral component remains poorly understood. This review examines how bacteriophages and eukaryotic viruses may influence immune regulation, glucose homeostasis, and diabetes. A structured narrative search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar was conducted. English-language studies published between 1996 and October 2026 were considered, with emphasis on literature published from 2010 onward. Human studies have reported altered phage abundance and disease-associated phage operational taxonomic units in type 2 diabetes. Reduced virome diversity has also been observed before islet-autoantibody seroconversion in children at risk of type 1 diabetes. Mechanistically, viral nucleic acids and phage-derived components may activate Toll-like receptor, NF-κB, MAPK, and interferon signaling. Phages may also reshape bacterial communities through lysis, lysogeny, and horizontal gene transfer. These changes could influence intestinal barrier integrity, microbial translocation, and the production of short-chain fatty acids, secondary bile acids, and tryptophan-derived metabolites. These pathways may, in turn, affect inflammation and insulin signaling. However, available studies vary considerably in design, sampling methods, virome-analysis techniques, and metabolic outcomes. Most evidence remains associative, indirect, or preclinical. Direct causal relationships between specific virome patterns, diabetes, and postprandial glucose responses have not been established. Longitudinal multi-omics studies incorporating continuous glucose monitoring and controlled experimental models are needed to clarify causality and determine the predictive and therapeutic value of the gut virome.
Cervical vertebral maturation (CVM) assessment is clinically important for evaluating skeletal maturity in orthodontic treatment planning; however, manual staging is subjective and affected by inter-observer variability. This study presents a deep learning framework for six-class CVM classification from lateral cephalometric radiographs using architecture comparison, distribution-aware multi-task learning, prototype learning, and cross-architecture soft voting. The dataset consisted of 1749 radiographs from 1749 patients with CVM stage labels assigned by consensus of two oral and maxillofacial radiologists. MaxViT-Small, Swin-Base, ConvNeXt-Base, and EfficientNetV2-L were first evaluated using five-fold cross-validation under a CVM-only hard-label cross-entropy setting, and EfficientNetV2-L was used as the anchor backbone for a full factorial ablation of three components: Gaussian soft-label learning with Kullback–Leibler divergence, auxiliary sex prediction, and prototype learning. The components did not act independently: Gaussian soft labels improved class-balanced performance only when combined with the auxiliary sex task, and the complete formulation reached 77.82% accuracy, 60.89% Macro-F1, and 59.03% balanced accuracy, whereas the separate contribution of prototype learning could not be resolved at this sample size. The same final formulation was subsequently applied to four distinct backbones and combined using equal-weight cross-architecture soft voting. The ensemble achieved 79.82% accuracy (95% confidence interval 77.93–81.65%), 62.49% Macro-F1, 59.22% balanced accuracy, and a quadratic weighted kappa of 0.908, with 97.77% of predictions within one stage of the reference. Relative to the best single model, the gains in accuracy and ordinal agreement were statistically supported, whereas those in Macro-F1 and balanced accuracy were not. An ensemble of independently trained models of a single backbone did not reproduce the accuracy gain, which is consistent with a benefit from architectural diversity; the necessity of all four members was not established. The gap of approximately 20 percentage points between overall and balanced accuracy reflects the predominance of CS6 (61.41%) and the low recall of the least represented early stages. All values are internal cross-validation estimates, and external validation is required before the framework can be considered for clinical use.
