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Chronic neuropathic ulcers remain a debilitating complication of leprosy, with limited evidence for effective treatments. Honey has been recommended to promote wound healing in other chronic ulcers. However, its efficacy in ulcer healing has not been rigorously evaluated. This dual-centre, prospective, single-blinded, randomised controlled trial compared raw honey dressings (n = 65) versus standard saline dressings (n = 65) for leprosy-associated foot ulcers in Nigeria. Participants with ulcers (2-20 cm2, ≥ 6 weeks duration) were randomised 1:1, stratified by ulcer size. Primary outcomes were complete healing by 84 days and healing rate, assessed through blinded digital planimetry. Secondary outcomes included ulcer recurrence and/or new ulcer development at 6 months. A total of 130 participants were randomised in the study. Complete healing occurred in 29.2% of honey-treated ulcers versus 24.6% with saline (adjusted HR 1.26, 95% CI 0.64-2.47). At 6 months, recurrence rates were similar (honey 13.5% vs saline 10.2%). The honey group showed a non-significant trend toward faster healing (p = 0.076). No treatment-related adverse events occurred. While honey dressings showed a modest advantage in healing rate, the difference was not statistically significant. The results suggest honey may be a safe, culturally acceptable option in resource-limited settings. This study provides high-quality data for inclusion in future systematic reviews. ISRCTN10093277. Registered on 22 December 2021.

Rainwater may be contaminated by leaching from roofing materials and industrial activities, rendering it unsuitable for potable use. In this study, the physico-chemical, microbiological and heavy metals parameters were investigated in harvested rainwater quality from various roof types in the urban and rural areas of Enugu State, Nigeria, between April-July, 2018. Metal compositions of asbestos, corrugated iron, Cameroun zinc, Stone-coated tiles and long span aluminium were also assessed. Thirty-six (36) harvested rainwater samples were forwarded to the laboratory after determination of pH, temperature, TDS and electrical conductivity at the point of collection. Titration, Electrometric and spectrophotometric methods of analysis were used. Results were compared with the WHO, USEPA and NSDWQ water quality standards. The weighted arithmetic method was obtained for the assessment of the water quality index. Results for heavy metals (mg/L) gave values which ranged from 0.00 to 0.20, metal concentrations (mg/kg) of digested roofs gave 0.00-63.78, and other parameters gave 6.50–7.60 for pH and Escherichia coli (no detection). The obtained results met the national and global standards except for a few samples. The WQI gave values which ranged from 3.00 to 78.46 and were rated excellent to very poor water quality. It was concluded that harvested rainwater should be treated before drinking.

ABSTRACT The 2019 and 2020 sporadic outbreaks of yellow fever (YF) in Sub-Saharan African countries had raised a lot of global health concerns. This article aims to narratively review the vector biology, YF vaccination program, environmental factors and climatic changes, and to understand how they could facilitate the reemergence of YF. This study comprehensively reviewed articles that focused on the interplay and complexity of YF virus (YFV) vector diversity/competence, YF vaccine immunodynamics and climatic change impacts on YFV transmission as they influence the 2019/2020 sporadic outbreaks in Sub-Saharan Africa (SSA). Based on available reports, vectorial migration, climatic changes and YF immunization level could be reasons for the re-mergence of YF at the community and national levels. Essentially, the drivers of YFV infection due to spillover are moderately constant. However, changes in land use and landscape have been shown to influence sylvan-to-urban spillover. Furthermore, increased precipitation and warmer temperatures due to climate change are likely to broaden the range of mosquitoes' habitat. The 2019/2020 YF outbreaks in SSA is basically a result of inadequate vaccination campaigns, YF surveillance and vector control. Consequently, and most importantly, adequate immunization coverage must be implemented and properly achieved under the responsibility of the public health stakeholders. The 2019 and 2020 yellow fever (YF) virus infection reemergence in some Sub-Saharan African countries reflects the interplay and complexity of Aedes mosquitoes' competence, inadequate YF vaccine coverage, environmental factors and climatic changes.

This study addresses a lexical ambiguity issue in Sesotho sa Leboa that arises from terms with various meanings, often known as homonyms or polysemous words. When compared to, for instance, European languages, this lexical ambiguity in Sesotho sa Leboa causes computational semantic problems in NLP when trying to identify the lexicon of a language. In other words, it is challenging to determine the proper lexical category and sense of words due to this ambiguity problem. In order to address the issue of polysemy in the Sesotho sa Leboa language, this study set out to create a word sense discrimination (WSD) scheme using a corpus-based hybrid transformer-based architecture and deep learning models. Additionally, the performance of baseline and improved machine learning models for a sequence-based natural language processing (NLP) task was assessed and compared. The baseline models included RNN-LSTM, BiGRU, LSTMLM, DeBERTa, and DistilBERT, with accuracies of 61%, 79%, 74%, 70%, and 64%, respectively. Among these, BiGRU emerged as the strongest performer, leveraging its bidirectional architecture to achieve the highest baseline accuracy. Transformer-based models, such as DeBERTa and DistilBERT, demonstrated moderate performance, with the latter prioritizing efficiency at the cost of accuracy. The enhanced results explored optimization techniques and hybrid model architectures to improve performance. BiGRU, optimized with ADAM, achieved an accuracy of 84%, while BiGRU with attention mechanisms further improved to 85%, showcasing the effectiveness of these enhancements. Hybrid models integrating BiGRU with transformer architectures demonstrated varying results. BiGRU + DeBERTa and BiGRU + ALBERT achieved the highest accuracies of 85% and 84%, respectively, highlighting the complementary strengths of bidirectional context modeling and advanced transformer-based contextual understanding. Conversely, the Hybrid BiGRU + RoBERTa model underperformed, with an accuracy of 70%, indicating potential mismatches in model synergy. These findings highlight how crucial hybridization and optimization are to reaching cutting-edge performance on NLP tasks. According to this study’s findings, the most promising approaches for fusing accuracy and efficiency are attention-based BiGRU and BiGRU–transformer hybrids, especially those that incorporate DeBERTa and ALBERT. To further improve speed, future research should concentrate on exploring task-specific optimizations and improving hybrid model integration.

Tanzania has implemented the Maternal and Perinatal Death Surveillance and Response (MPDSR) system for more than a decade. However, multiple assessments have shown that the quality of maternal death reviews at facility and district levels has often been limited by inadequate specialist participation, weak root-cause analysis, and insufficient follow-through on action plans. These limitations have reduced the effectiveness of MPDSR in driving quality improvement and preventing avoidable maternal deaths. To address these gaps and strengthen accountability and clinical learning, the Ministry of Health introduced daily Virtual Maternal Death Reviews (VMDR) in 2021. This study describes the implementation and outcomes of VMDR from 2022 to 2023. A descriptive observational study was conducted using a mixed-methods approach. The analysis included maternal deaths that were notified through the national MPDSR system and subsequently selected for VMDR sessions between January 2022 and December 2023. Cases were eligible if they met the WHO definition of a maternal death and had sufficient clinical documentation from facility or district review committees to allow determination of the cause of death and contributing factors. Quantitative data were summarised using descriptive statistics, while qualitative insights from VMDR discussions were synthesised to identify modifiable clinical and system-level factors and response actions. A total of 369 VMDR sessions were conducted, reviewing 687 maternal deaths across all regions. The leading causes of death were obstetric haemorrhage (49%), hypertensive disorders of pregnancy (16%), and anaesthesia-related complications (10%). Common modifiable contributing factors included inadequate clinical competency (82%), suboptimal provider practices and attitudes (69%), weak leadership and accountability (42%), and gaps in surgical and anaesthesia care. VMDR participation facilitated improved oversight and prompted remedial actions such as redeployment of specialists, improvement of essential supplies, and initiation of structured mentorship and continuing medical education. The VMDR model strengthened the quality and consistency of maternal death reviews and improved accountability in responding to identified gaps. Sustaining VMDR requires continued leadership to maintain confidentiality in line with existing culture and norms.

Fluoxetine (FLX) is a Selective Serotonin Re-uptake Inhibitor (SSRI) commonly used as a first-line treatment for depression, anxiety, and mood disorders. It can cause infertility in the male reproductive system through the release of Reactive Oxygen Species (ROS). This study aimed to evaluate the testiculo-protective potential of ascorbic acid against fluoxetine-induced spermatotoxicity in male Wistar rats. This study assessed Vitamin C's effect on male fertility in fluoxetine-treated Wistar rats. Thirty rats (130 ± 40 g) were divided into six groups (n=5): Control (distilled water), fluoxetine 20 mg/kg, Vitamin C 100 mg/kg, fluoxetine 20 mg/kg + Vitamin C 50 mg/kg, fluoxetine 20 mg/kg + Vitamin C 100 mg/kg, and fluoxetine 20 mg/kg + Vitamin C 150 mg/kg. Treatments were administered daily via oral gavage for 60 days, followed by assessments of testicular weight, semen analysis, oxidative stress biomarkers (CAT and GPx), and histomorphology. The data was analyzed using one-way ANOVA and Turkey's post-hoc multiple comparison test, reporting as mean±SEM using The GraphPad Prism version 6.0 for Windows, with significance set at p<0.05. Vitamin C, administered particularly at higher doses, significantly increased body weight, testicular weight, and antioxidant enzyme levels (glutathione peroxidase and catalase) while improving fertility parameters such as sperm count, motility, and viability in treated rats (P<0.05). Fluoxetine alone led to a significant reduction (P<0.05) in these parameters, but the combination with Vitamin C mitigated these effects. Histological analysis showed improved testicular structure in Vitamin C-treated groups, highlighting its protective role against fluoxetine-induced testicular damage. Ascorbic acid has testiculoprotective potential in fluoxetine-induced spermatotoxicity, mainly owing to its antioxidant properties.

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.

A tripodal Schiff base ligand, 2,4,6-Tris(4-carboxybenzimino)-1,3,5-triazine (MT) and its trinuclear Dy(III), Er(III), and Gd(III) complexes were synthesized. These were characterized using UV-visible, IR, 1H, and 13C NMR spectroscopies, elemental analysis, and molar conductivity measurements. The spectral studies indicate that the ligand is hexadentate and coordinates to the Ln(III) ions through the oxygen atoms of the carboxylic group. The trinuclear complexes were characterized as being bridged by carboxylate anions to the Dy(III), Er(III), and Gd(III) salen centers and displaying a coordination number of six. Biological studies revealed that MT is more active against the test micro-organisms relative to the trinuclear complexes. Acute toxicity studies revealed that MT is safe and has a wide range of effective doses (ED50). In vivo antimalarial studies indicate that MT could serve as an effective antimalarial agent since it has parasitemia inhibition of 84.02% at 50 mg/kg and 65.81% at 25 mg/kg, close to the value (87.22%) of the standard drug—Artesunate. Molecular docking simulation studies on the compounds against SARS-CoV-2 (6Y84) and E. coli DNA gyrase (5MMN) revealed effective binding interactions through multiple bonding modes. The binding energy calculated for Er(III)MT-6Y84 and Er(III)MT-5MMN complexes showed active molecules with the ability to inhibit SARS-CoV-2 and E. coli DNA gyrase.

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R2), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R2 for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis.