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Arbuscular Mycorrhizal Fungi (AMF) and Rhizobium (RHZ) are key bio-inoculants in sustainable agriculture, known for their symbiotic relationships with plants. However, their effects on soil functions under different proportions of inorganic fertilizers are not well understood. This study, conducted during the Rabi seasons from 2019 to 2021 in alluvial soils of Punjab, India, investigates the impact of AMF and RHZ inoculation on root morphology and rhizospheric soil chemical properties in field pea (Pisum sativum L.). The findings indicate that dual inoculation with AMF and RHZ (RHZ + AMF + N50%+P50%+K100%) significantly enhances root growth and improves soil chemical properties. Despite an initial increase in pH that negatively affected micronutrient availability at 60 days after sowing (DAS), a stabilizing trend at 90 DAS was observed, leading to better availability of Fe, Cu, Mn, and Zn, along with higher Cation Exchange Capacity and macronutrient availability. This dual inoculation strategy is found to maximize profitability in terms of root morphology and soil chemical properties. Notably, lower root Cation Exchange Capacity compared to soil Cation Exchange Capacity may be due to factors like soil structure and root interactions. Principal Component Analysis (PCA) of soil parameters effectively distinguishes between treatments, showing that RHZ and AMF respond differently to various NPK proportions. For instance, treatments T3 (RHZ + N50% + P100% + K100%), T4 (RHZ + N75% + P100% + K100%), and T6 (AMF + N100% + P75% + K100%) are grouped together, while treatments T5 (AMF + N100% + P50% + K100%) and T7 (RHZ + AMF + N50% + P50% + K100%) cluster separately. This suggests that dual inoculation, especially as seen in Treatment T7, is recommended for sustained soil health and enhanced productivity.

Malaria treatment failures associated with reduced efficacy of chloroquine (CQ) and amodiaquine (AQ) antimalarial drugs emerged in Rwanda during the 1980s, prompting the policy shift towards adopting artemisinin-based combination therapies in 2006 as an alternative. However, recent findings from malaria surveillance and therapeutic efficacy studies have revealed a countrywide increase in antimalarial drug resistance. Particularly, artemether-lumefantrine (AL) efficacy has significantly decreased, probably due to the emergence of Plasmodium falciparum (Pf) genomic mutations. To mitigate the current drug resistance, Rwanda has adopted targeted multiple first-line therapies. Through the national malaria control program, antimalarial drugs were deployed in accordance with the reported resistance profile. A significant rise in Pfkelch13 mutations, particularly A675V associated with AL resistance, was mainly reported in the western region; therefore, artesunate-pyronaridine was recommended. Dihydroartemisinin-piperaquine was considered in eastern and central regions, where R561H mutations were predominant. On the contrary, AL was maintained in the southern region, where the prevalence of the R561H mutation was low. Insights from this data-driven model will inform its extension to other malaria-endemic countries facing emerging Pf genetic diversity.

Background Unsafe abortion remains a critical global health issue, particularly affecting women in low and middle-income countries including Rwanda. In Africa, 99% of abortions are unsafe. Between 2019 and 2023, there has been a reported increased of abortion-related hospitalizations at Remera Rukoma Hospital (RRH), highlighting an urgent need for investigation. Therefore, this study was conducted to determine the prevalence and factors associated with unsafe abortion among women of reproductive age attending RRH. Methods This cross-sectional study was conducted among 384 women of reproductive age (15–49 years) attending at RRH in Rwanda. The sample size was obtained using the Cochrane formula and systematic random sampling. Data analysis utilized SPSS Version25, employing descriptive statistics, bivariate and multivariate analyses to determine the factors associated with unsafe abortion at 95% CI, and p-value < 0.05 was considered statistically significant. Results The majority of participants were married (51.8%), Catholic (32.8%), attained primary education (35.4%), and were housewives (42.2%). The prevalence of unsafe abortion was 35.2%. Multivariate analysis showed unsafe abortion was higher than twice among women married before 18 years (aOR = 2.277, 95% CI: 1.247–4.157, p  = 0.007), those with 3 sexual partners in the last 12 months (aOR = 2.285, 95% CI: 1.031–5.066, p  = 0.042). Women who experienced gender-based violence had higher odds of engaging in unsafe abortion (aOR = 1.965, 95% CI: 1.128–3.424, p  = 0.017). Conclusion This study revealed unsafe abortion as a significant health concern among women of reproductive age, with over one-third of participants reporting unsafe abortions. Early marriage before 18 years, multiple sexual partners, and gender-based violence emerged as key factors associated with unsafe abortion practices. Addressing this issue requires a multifaceted approach, community education, and targeted initiatives to combat gender-based violence and early marriage in Rwanda.

Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients’ needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences. We extracted data on all COVID-19 cases and exposed contacts who were enrolled in the WelTel text messaging program between March 18, 2020, and March 31, 2022, and linked demographic and clinical data from the national COVID-19 registry. A sample of the text conversation corpus was English-translated and labeled with topics of interest defined by medical experts. Multiple natural language processing (NLP) topic classification models were trained and compared using F1 scores. Best performing models were applied to classify unlabeled conversations. Total 33,081 isolated patients (mean age 33·9, range 0–100), 44% female, including 30,398 cases and 2,683 contacts) were registered in WelTel. Registered patients generated 12,119 interactive text conversations in Kinyarwanda (n = 8,183, 67%), English (n = 3,069, 25%) and other languages. Sufficiently trained large language models (LLMs) were unavailable for Kinyarwanda. Traditional machine learning (ML) models outperformed fine-tuned transformer architecture language models on the native untranslated language corpus, however, the reverse was observed of models trained on English-only data. The most frequently identified topics discussed included symptoms (69%), diagnostics (38%), social issues (19%), prevention (18%), healthcare logistics (16%), and treatment (8·5%). Education, advice, and triage on these topics were provided to patients. Interactive text messaging can be used to remotely support isolated patients in pandemics at scale. NLP can help evaluate the medical and social factors that affect isolated patients which could ultimately inform precision public health responses to future pandemics.