Background: Small non-coding RNAs play critical regulatory roles in post-transcription. However, their characteristics in Trichomonas vaginalis, the causative agent of human sexually transmitted trichomoniasis, still remain to be determined. Methods: Small RNA transcriptomes from Trichomonas trophozoites were deep sequenced using the Illumina NextSeq 500 system and comprehensively analyzed to identify Trichomonas microRNAs (miRNAs) and transfer RNA (tRNA)-derived small RNAs (tsRNAs). The tsRNA candidates were confirmed by stem-loop quantitative reverse transcription-PCR, and motifs to guide the cleavage of tsRNAs were predicted using the GLAM2 algorithm. Results: The miRNAs were found to be present in T. vaginalis but at an extremely low abundance (0.0046%). Three categories of endogenous Trichomonas tsRNAs were identified, namely 5′tritsRNAs, mid-tritsRNAs and 3′tritsRNAs, with the 5′tritsRNAs constituting the dominant category (67.63%) of tsRNAs. Interestingly, the cleavage site analysis verified both conventional classes of tRNA-derived fragments (tRFs) and tRNA-halves in tritsRNAs, indicating the expression of tRNA-halves in the non-stress condition. A total of 25 tritsRNAs were experimentally confirmed, accounting for 78.1% of all tested candidates. Three motifs were predicted to guide the production of tritsRNAs. The results prove the expression of tRFs and tRNA-halves in the T. vaginalis transcriptome. Conclusions: This is the first report of genome-wide investigation of small RNAs, particularly tsRNAs and miRNAs, from Trichomonas parasites. Our findings demonstrate the expression profile of tsRNAs in T. vaginalis, while miRNA was barely detected. These results may promote further research aimed at gaining a better understanding of the evolution of small non-coding RNA in T. vaginalis and their functions in the pathogenesis of trichomoniasis. Graphical

Plasmodium vivax remains the predominant species at the China–Myanmar border, imposing a major challenge to the recent gains in regional malaria elimination. To closely supervise the emerging of drug resistance in this area, we surveyed the variations in genes potentially correlated with drug resistance in P. vivax parasite and the possible drug selection with time. A total of 235 P. vivax samples were collected from patients suffering uncomplicated malaria at Yingjiang, Tengchong, and Longling counties, and Nabang port in China, Yunnan province, and Laiza sub-township in Myanmar, from 2008 to 2017. Five potential drug resistance genes were amplified utilizing nested-PCR and analyzed, including pvdhfr, pvdhps, pvmdr1, pvcrt-o, and pvk12. The Pearson’s Chi-squared test or Fisher’s exact test were applied to determine the statistical frequency differences of mutations between categorical data. The pvdhfr F57I/L, S58R, T61M and S117T/N presented in 40.6%, 56.7%, 40.1%, and 56.0% of the sequenced P. vivax isolates, and these mutations significantly decreased with years. The haplotype formed by these quadruple mutations predominated in Yingjiang, Tengchong, Longling and Nabang. While a mutation H99S/R (56.6%) dominated in Laiza and increased with time. In pvdhps, the A383G prevailed in 69.2% of the samples, which remained the most prevalent haplotype. However, a significant decrease of its occurrence was also noticed over the time. The S382A/C and A553G existed in 8.4% and 30.8% of the isolates, respectively. In pvmdr1, the mutation Y976F occurred at a low frequency in 5/232 (2.2%), while T958M was fixed and F1076L was approaching fixed (72.4%). The K10 insertion was detected at an occurrence of 33.2% in pvcrt-o, whereas there was no significant difference among the sites or over the time. No mutation was identified in pvk12. Mutations related with resistance to antifolate drugs are prevalent in this area, while their frequencies decrease significantly with time, suggestive of increased susceptibility of P. vivax parasite to antifolate drugs. Resistance to chloroquine (CQ) is possibly emerging. However, since the molecular mechanisms underneath CQ resistance is yet to be better understood, close supervision of clinical drug efficiency and continuous function investigation is urgently needed to alarm drug resistance.

A new modified three-dimensional (3D) nonlinear Schrödinger equation (3DMNLSE) is derived for gravity waves in the presence of wind, dissipation, and two-dimensional slowly varying currents, which include transverse and longitudinal currents in finite water depth. The effect of currents on modulational instability (MI) is investigated. The divergence (convergence) effect of longitudinal favorable (adverse) currents decreases (increases) the MI growth rate and region of instability while suppressing (enhancing) the formation of freak waves. Meanwhile, the transverse currents hardly affect the occurrence of MI. Furthermore, some results from the simulations with the space evolution of 3DMNLSE are presented. The results show the ubiquitous occurrence of freak waves in 3D wave fields under certain sets of initial conditions. We demonstrate that larger waves can be triggered when a weakly modulated wave train enters a region of adverse currents. The maximum amplitude of a freak wave depends on the ratio of the current velocity to the wave phase velocity.

Objective: This single-center, cross-sectional study investigates the health management demands, service utilization patterns, and their determinants among the older adults in a community of Liuzhou City, to provide empirical evidence for optimizing community-based older adults health services. With rising retirement ages and the increasing proportion of older workers in China’s labor force, this study also examines how work status and schedule compatibility shape service utilization. Methods: Using a convenience sampling method, a questionnaire survey was conducted among 320 older adults individuals aged 60 and above in the jurisdiction of a community health service center in Liuzhou City in 2025. Grounded in Andersen’s Behavioral Model, the survey covered predisposing, enabling, and need factors, alongside service utilization. Data were analyzed using descriptive statistics, chi-square tests, and binary logistic regression. Results: The chronic disease prevalence was 68.4%. While 82.5% were aware of the available community health services, only 45.6% regularly participated. The top three desired services were routine physical examinations (91.2%), chronic disease management (87.5%), and home-based medical services (65.3%). Regression analysis indicated that education level (OR = 2.35, 95% CI: 1.21–4.56), monthly income (OR = 1.98, 95% CI: 1.05–3.73), number of chronic diseases (OR = 3.11, 95% CI: 1.67–5.79), and co-residence with family (OR = 0.45, 95% CI: 0.22–0.92) were significant predictors of active participation. Need factors were the primary driver of participation, while enabling factors acted as critical facilitators or barriers. Conclusion: A significant “high demand, low utilization” paradox exists. These findings highlight a need for targeted, locally-tailored strategies focusing on vulnerable groups (e.g., low-income, single-living, less-educated older adults) in Liuzhou. Strengthening health education, diversifying services (especially home-based medical services), and integrating family support may enhance the effectiveness of community health management programs. However, further research in diverse settings is required to confirm the generalizability of these conclusions.

Objectives: This study aims to harness Natural Language Processing (NLP) to improve chronic disease medication management, focusing on the antihypertensive drug Lisinopril. The objectives are: (1) to develop an intelligent drug safety monitoring and personalized intervention platform integrating patient feedback; (2) to accurately identify drug side effects and analyze their associations with patient demographics; and (3) to build a personalized medication recommendation system for enhanced clinical decision-making. Methods: We used user reviews from authoritative medical databases (WebMD and Kaggle) spanning 2008–2020, forming a high-quality dataset of 345,845 samples related to Lisinopril. Data preprocessing involved redundancy removal, missing value imputation (e.g., using the KNN algorithm), and text standardization (e.g., through UMLS terminology mapping). NLP techniques included sentiment analysis and entity recognition with BERT models, topic modeling using LDA algorithms to extract key side-effect patterns (e.g., dry cough, dizziness), as well as the integration of knowledge graphs and decision trees for personalized recommendations, and the development of an interactive risk heatmap dashboard for dynamic monitoring. Results: The analysis revealed that common side effects of Lisinopril included dry cough (38%), dizziness (28%), and fatigue (22%), with nearly half of users experiencing these issues—particularly among older adults and long-term users. Sentiment analysis showed that 58% of reviews were negative, primarily due to side effects. The personalized recommendation system, tested in simulated scenarios, significantly improved medication adherence by suggesting alternatives (e.g., ARBs for cough-sensitive patients). The NLP-driven framework achieved 89.7% accuracy in sentiment classification and identified side-effect patterns 15 times faster than manual annotation. Conclusion: NLP effectively extracts insights from patient feedback, enabling rapid identification of drug efficacy and side effects. The study demonstrates the potential of NLP to support chronic disease management and precision medicine by facilitating early risk detection and personalized interventions. Future work should address subjective bias in reviews and integrate multi-source data for comprehensive assessment models.