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Introduction Depression is prevalent among aging people living with HIV (PLWH) worldwide. We sought to identify depression risk factors among a group of middle-aged and older PLWH in Lima, Peru. Materials and Methods We assessed risk factors for depression among PLWH over age 40 receiving care in an HIV clinic in Lima, Peru. The Patient Health Questionnaire-9 (PHQ-9) was administered. We performed descriptive statistics and logistic regression analyses. Results Mean age was 51.7 ± 7.7 years with 15.3% females. One-quarter of participants had depression with higher frequency in females. Risk factors that significantly increased the risk of depression included female sex (adjusted prevalence ratio [aPR] = 2.19 [95%CI 1.07-4.49]), currently smoking (aPR = 2.25 [95%CI 1.15-4.43]), and prior opportunistic infection (aPR = 2.24 [95%CI 1.05-4.76]). Discussion Our study demonstrates that PLWH who are female, current smokers, or had an opportunistic infection have higher risk of depression. Identifying PLWH at-risk for depression is key to early mental health interventions.

Abstract Periodontitis is a chronic inflammatory disease that affects the tissues surrounding the teeth, including the gums and the bones supporting the teeth. Early detection and intervention are crucial for effective management of periodontitis. Our study aims to identify a diagnostic biomarker for periodontitis and explore the pathways associated with the occurrence and development of periodontitis. The expression of gingival tissue from periodontitis and healthy control were downloaded from the Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) were used to analyze module genes associated with periodontitis and DESeq2 were performed to identify differently expressed genes (DEGs) between periodontitis and healthy control. Then the candidate genes were obtained by intersecting the genes from interest modules and DEGs. Functional enrichment analysis was performed using gene ontology and kyoto encyclopedia of gene and genomes, followed by the protein–protein interaction (PPI) network analysis. The hub genes were identified by the cytoCNA plugin in Cytoscape. Finally, immunohistochemical staining of the hub genes was performed to validate the findings. WGCNA analysis found that the expression of the MEblack module was significantly higher in individuals with periodontitis compared to those in the healthy control group. A total of 888 DEGs, including 750 upregulated and 138 downregulated genes, were identified. Finally, 427 candidate genes were identified potentially associated with periodontitis after intersecting the DEGs and the black module genes. Several critical signaling pathways were identified associated with periodontitis by functional enrichment analysis, including cytokine–cytokine receptor interaction, neutrophil extracellular trap formation, Staphylococcus aureus infection, and Interleukin-17 signaling pathway. The PPI network analysis revealed that C-X-C motif chemokine ligand 5 (CXCL5) and C-X-C motif chemokine ligand 6 (CXCL6) could play an important role in the process of periodontitis. The gene expression level of CXCL5 and CXCL6 detected using immunohistochemical verified the findings. In conclusion, we found that CXCL5 and CXCL6 are closely associated with the occurrence of periodontitis. Our present pilot study suggests that CXCL5 and CXCL6 have the potential to be used as a diagnostic biomarker of periodontitis.

O’nyong-nyong virus (ONNV) is a neglected mosquito-borne alphavirus belonging to the Togaviridae family. ONNV is known to be responsible for sporadic outbreaks of acute febrile disease and polyarthralgia in Africa. As climate change increases the geographical range of known and potential new vectors, recent data indicate a possibility for ONNV to spread outside of the African continent and grow into a greater public health concern. In this review, we summarise the current knowledge on ONNV epidemiology, host–pathogen interactions, vector-virus responses, and insights into possible avenues to control risk of further epidemics. In this review, the limited ONNV literature is compared and correlated to other findings on mainly Old World alphaviruses. We highlight and discuss studies that investigate viral and host factors that determine viral-vector specificity, along with important mechanisms that determine severity and disease outcome of ONNV infection.

Excessive aluminum (Al) in acidic soils is a primary factor that hinders plant growth. The objective of the present study was to investigate the effect and physiological mechanism of exogenous silicon (Si) in alleviating aluminum toxicity. Under hydroponic conditions, 4 mM Al significantly impeded the growth of white clover; however, pretreatments with 1 mM Si mitigated this inhibition, as evidenced by notable changes in growth indicators and physiological parameters. Exogenous silicon notably increased both shoot and root length of white clover and significantly decreased electrolyte leakage (EL) and malondialdehyde (MDA) content compared to aluminum treatments. This positive effect was particularly evident in the roots. Further analysis involving hematoxylin staining, scanning electron microscopy (SEM), and examination of organic acids (OAs) demonstrated that silicon relieved the accumulation of bioactive aluminum and ameliorated damage to root tissues in aluminum-stressed plants. Additionally, energy-dispersive X-ray (EDX) analysis revealed that additional silicon was primarily distributed in the root epidermal and cortical layers, effectively reducing the transport of aluminum and maintaining the balance of exchangeable cations absorption. These findings suggest that gradual silicon deposition in root tissues effectively prevents the absorption of biologically active aluminum, thereby reducing the risk of mineral nutrient deficiencies induced by aluminum stress, promoting organic acids exudation, and compartmentalizing aluminum in the outer layer of root tissues. This mechanism helps white clover alleviate the damage caused by aluminum toxicity.

Underwater image super-resolution is vital for enhancing the clarity and detail of underwater imagery, enabling improved analysis, navigation, and exploration in underwater environments where visual quality is typically degraded due to factors like water turbidity and light attenuation. In this paper, we propose an effective hybrid dynamic Transformer (called HDT-Net) for underwater image super-resolution, leveraging a collaborative exploration of both local and global information aggregation to help image restoration. Firstly, we introduce a dynamic local self-attention to adaptively capture important spatial details in degraded underwater images by employing dynamic weighting. Secondly, considering that visual transformers tend to introduce irrelevant information when modeling the global context, thereby interfering with the reconstruction of high-resolution images, we design a sparse non-local self-attention to more accurately compute self-similarity by setting a top-k threshold. Finally, we integrate these two self-attention mechanisms into the hybrid dynamic transformer module, constituting the primary feature extraction unit of our proposed method. Quantitative and qualitative analyses on benchmark datasets demonstrate that our approach achieves superior performance compared to previous CNN and Transformer models.

Adverse weather conditions such as rain and haze often lead to a degradation in the quality of maritime images, which is crucial for activities like navigation, fishing, and search and rescue. Therefore, it is of great interest to develop an effective algorithm to recover high-quality maritime images under adverse weather conditions. This paper proposes a prompt-based learning method with degradation perception for maritime image restoration, which contains two key components: a restoration module and a prompting module. The former is employed for image restoration, whereas the latter encodes weather-related degradation-specific information to modulate the restoration module, enhancing the recovery process for improved results. Inspired by the recent trend of prompt learning in artificial intelligence, this paper adopts soft-prompt technology to generate learnable visual prompt parameters for better perceiving the degradation-conditioned cues. Extensive experimental results on several benchmarks show that our approach achieves superior restoration performance in maritime image dehazing and deraining tasks.

Background A prescribing monitoring policy (PMP) was implemented in November 2015 in Anhui province, China, the first province to pilot this policy to manage the use and costs of select drugs based on their large prescription volumes and/or costs in hospitals. This study evaluated the impact of PMP on the use and expenditures of different drugs in three tertiaryhospitals in Anhui.Methods We obtained monthly drug use and expenditures data from three tertiary hospitals in Anhui (November 2014 through September 2017). An interrupted time series (ITS) design was used to estimate changes in defined daily doses (DDDs per month) and drug expenditures (dollars per month) of policy-targeted and non-targeted drugs after PMP implementation. Drugs were grouped based on whether they were recommended (recommended drugs) by any clinical guidelines or not (non-recommended drugs), or if they were potentially over-used (proton pump inhibitors, PPIs).Results After PMP, DDDs and costs of the targeted PPIs (omeprazole) declined while use of non-targeted PPIs increased correspondingly with overall sustained declines in total PPIs. The policy impact on recommended drugs varied based on whether the targeted drugs have appropriate alternatives. The DDDs and costs of recommended drugs that have readily accessible appropriate alternatives (atorvastatin) declined, which offset increases in its alternative non-target drugs (rosuvastatin), while there was no significant change in those recommended drugs that did not have appropriate alternative drugs (clopidogrel and ticagrelor). Finally, the DDDs and costs of non-recommended drugs decreased significantly.Conclusion PMP policy impact was not the same across different drug groups. PMP did help contain the use and costs of potentially over-used drugs and non-recommended drugs. PMP did not seem to reduce the use of first-line therapeutic drugs recommended by clinical treatment guidelines, especially those lacking alternatives; such drugs are unlikely appropriate candidates for PMP.

Abstract Spermatogenesis is a complex process related to male infertility. Till now, the critical genes and specific mechanisms have not been elucidated clearly. Our objective was to determine the hub genes that play a crucial role in spermatogenesis by analyzing the differentially expressed genes (DEGs) present in non-obstructive azoospermia (NOA) compared to OA and normal samples using bioinformatics analysis. Four datasets, namely GSE45885, GSE45887, GSE9210 and GSE145467 were used. Functional enrichment analyses were performed on the DEGs. Hub genes were identified based on protein–protein interactions between DEGs. The expression of the hub genes was further examined in the testicular germ cell tumors from the TCGA by the GEPIA and validated by qRT-PCR in the testes of lipopolysaccharide-induced acute orchitis mice with impaired spermatogenesis. A total of 203 DEGs including 34 up-regulated and 169 down-regulated were identified. Functional enrichment analysis showed DEGs were mainly involved in microtubule motility, the process of cell growth and protein transport. PRM2, TEKT2, FSCN3, UBQLN3, SPATS1 and GTSF1L were identified and validated as hub genes for spermatogenesis. Three of them (PRM2, FSCN3 and TEKT2) were significantly down-regulated in the testicular germ cell tumors and their methylation levels were associated with the pathogenesis. In summary, the hub genes identified may be related to spermatogenesis and may act as potential therapeutic targets for NOA and testicular germ cell tumors.

Soil bacterial and fungal communities play key roles in the degradation of organic contaminants, and their structure and function are regulated by bottom-up and top-down factors. Microbial ecological effects of polycyclic aromatic hydrocarbons (PAHs) and trophic interactions among protozoa and bacteria/fungi in PAH-polluted soils have yet to be determined. We investigated the trophic interactions and structure of the microbiome in PAH-contaminated wasteland and farmland soils. The results indicated that the total concentration of the 16 PAHs (∑PAHs) was significantly correlated with the Shannon index, NMDS1 and the relative abundances of bacteria, fungi and protozoa (e.g., Pseudofungi) in the microbiome. Structural equation modelling and linear fitting demonstrated cascading relationships among PAHs, protozoan and bacterial/fungal communities in terms of abundance and diversity. Notably, individual PAHs were significantly correlated with microbe-grazing protozoa at the genus level, and the abundances of these organisms were significantly correlated with those of PAH-degrading bacteria and fungi. Bipartite networks and linear fitting indicated that protozoa indirectly modulate PAH degradation by regulating PAH-degrading bacterial and fungal communities. Therefore, protozoa might be involved in regulating the microbial degradation of PAHs by predation in contaminated soil.

Objective To report a case of xanthomatous hypophysitis (XH), and summarize its clinical manifestations, laboratory, imaging and histopathological features. Methods and Results The patient was a male XH diagnosed and treated by Hebei General Hospital in July 2018. The clinical manifestations were headache with nausea and vomiting. Head CT showed the sella area was dense and full; MRI showed enlargement of the sella and a type of circular mass within the sella. The clinical diagnosis was pituitary adenoma apoplexy, and pituitary adenoma resection was performed through nasal sphenoid approach under neuroendoscopy. The postoperative pathological examination showed the histopathological cells showed flake dense infiltration, the immunohistochemical staining showed strong positive CD68 and vimentin (Vim), and S ⁃100 protein and CD1α were negative, Ki⁃67 antigen labeling index was 10%-20%, and the final diagnosis was sellar XH. Conclusions XH is rare in clinic, and its symptoms and imaging findings are nonspecific, making clinical diagnosis extremely challenging. Neuroendoscopic biopsy pathology is the "gold standard" for diagnosis and differential diagnosis, and attention should be paid to distinguishing pituitary adenoma apoplexy when accompanied by bleeding.

The Schlieren technique is widely adopted for visualizing supersonic jets owing to its non-invasiveness to the flow field. However, extending the classical Schlieren method for quantitative refractive index measurements is cumbersome, especially for three-dimensional supersonic flows. Background-oriented Schlieren has gained increasing popularity owing to its ease of implementation and calibration. This study utilizes multi-view-based tomographic background-oriented Schlieren (TBOS) to reconstruct axisymmetric and weakly non-axisymmetric supersonic jets, highlighting the impact of flow axisymmetry breaking on TBOS reconstructions. Several classical TBOS reconstruction algorithms, including FDK, SART, SIRT, and CGLS, are compared quantitatively regarding reconstruction quality. View spareness is identified to be the main cause of degraded reconstruction quality when the flow experiences axisymmetry breaking. The classic visual hull approach is explored to improve reconstruction quality. Together with the CGLS tomographic algorithm, we successfully reconstruct the weakly non-axisymmetric supersonic jet structures and confirm that increasing the nozzle bevel angle leads to wider jet spreads.

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (