Summary: This study aimed to determine the effectiveness of mefl oquine alone or combined with albendazole in reduced doses against T. spiralis infection. One hundred and twenty albino mice were orally infected with 200 T. spiralis larvae/mouse. Drugs were administered during the enteral phase on days 1 to 3 and on the chronic phase on days 35 to 37 post-infection, and mice were sacrificed, respectively, at days 7 or 48 post-infection to count mature intestinal worms or encysted muscle larvae. The effect of the treatment on the histology of the target organs of each phase, intestine and diaphragm, was also evaluated. A signifi cant decrease in intestinal worms was found in all treated groups relative to the untreated control group at a peak of 93.7% in the combination albendazole-mefl oquine group. Results in all treated groups demonstrated a signifi cant decrease in muscle larvae relative to untreated control groups, achieving 86.2 % in the combined albendazole-mefl oquine group. There was a marked improvement in the intestinal and muscular architecture in all treated groups compared to the non-treated control group. Notably, the albendazole-mefl oquine group showed an almost complete recovery. The combined albendazole-mefl oquine low dose regimen had the highest effect on reducing parasite burden and restoring normal histological architecture.

Although praziquantel (PZQ) is the main antischistosomal drug currently in use, concerns remain regarding incomplete reversal of schistosomiasis-induced pathology and the emergence of drug resistance. This study evaluates the combined effect of PZQ with safranal, a bioactive saffron constituent, on Schistosoma mansoni-induced pathology in mice. Male CD1 Swiss albino mice were exposed to 60 S. mansoni cercariae and, at week 9 post-infection, were treated with PZQ (500 mg/kg orally for two consecutive days), safranal (50 mg/kg/day), or both, for three weeks. The animals were sacrificed at week 11 post-infection. Worm and egg burdens, liver histopathology, fibrotic markers, oxidative stress, and inflammatory cytokines were assessed. Combined PZQ + safranal therapy significantly reduced adult worm counts and hepatic and intestinal egg loads compared to PZQ alone. All treatments decreased liver index (hepatomegaly), with the combination treatment providing the best intervention. Histological analyses revealed significantly reduced granuloma size and hepatic necrosis post-treatment, particularly in the combination group. The levels of proinflammatory cytokines (TNF-α, IL-1β) and Th2 cytokines (IL-4, IL-5, IL-6, IL-10) were significantly lowered in treated mice, most notably with the combination treatment. Oxidative stress was also markedly attenuated, and infected mice exhibited elevated malondialdehyde and depleted antioxidant enzymes (SOD, CAT, GSH). Interestingly, PZQ and/or safranal restored antioxidant status and reduced lipid peroxidation, with the combination being most effective. Furthermore, collagen deposition and expression of hepatic fibrotic markers α-smooth muscle actin (α-SMA), TGF-β1, and matrix metalloproteinase-9 were most effectively suppressed by combined therapy. To conclude, safranal enhances PZQ’s antischistosomal efficacy and confers additive protection against Schistosoma-induced liver fibrosis.

This review integrates multimodal data, which includes genetic information, wearable sensor outputs, and electronic health records (EHRs), providing an innovative analysis of artificial intelligence (AI) advancements for chronic disease prediction. AI is revolutionizing chronic disease management, particularly for diabetes and cardiovascular disease. Through the incorporation of evidence from AI-enabled models, the research projects predictive accuracies exceeding 80% in the onset and progression of illness and their role in ensuring early diagnosis, customized treatment, and operational efficiency. Inventions like neural networks improved by particle swarm optimization attain diagnostic accuracies of 99.67%, while edge computing helps real-time monitoring to reduce hospitalizations through early interventions. Although AI in health care has advanced significantly, algorithmic biases against underrepresented populations—e.g., older adults and non-White communities—fragmented data ecosystems preventing institutional interoperability, and ethical issues regarding privacy and transparency continue to impede scalability and fair implementation. To overcome these obstacles and ensure innovations are distributed equitably across global health care systems, future initiatives should prioritize multimodal data fusion, fairness audits during model development, federated learning frameworks that support safe cross-institutional collaboration, and large clinical trials that validate AI in multiple real-world settings. The advancement of AI has an opportunity to significantly enhance the quality of life for those struggling with chronic diseases and transform health care systems globally, provided that its advancement strategies are transparent, equitable, and focused on wide-ranging validation projects. By combining different multimodal data (EHRs, wearables, and genomes) with fairness audits and federated learning, this review provides a novel synthesis of AI advances for the prediction of chronic diseases. This assessment stands out from others because it addresses both technical performance and ethical deployment strategies. The aim is to support efforts to generate AI adoption equitably in the real world.

Background: Cryptosporidium is a life-threatening intestinal pathogen in immunocompromised patients. Currently, there is no entirely successful drug against cryptosporidiosis. Several reports utilizing nanotechnology combined with herbal medicine were published to evaluate its efficacy in treatment of intestinal pathogens. Objectives: To evaluate the anti-cryptosporidial efficacy of Commiphora molmol extract and its conjugation with chitosan nanofibers (CsNFs) for treatment of cryptosporidiosis in mice. Material and Methods: Sixty five male albino mice were infected orally with 1×104 Cryptosporidium oocysts. They were grouped and treated with nanazoxid (NTZ) (100 mg/kg), C. molmol extract (500 mg/kg), and C. molmol loaded chitosan nanofibers (Cm-CsNFs) at 25, 50, and 100 mg/kg. The scarification of mice was done after 5 and 10 days of treatments. Assessment of the treatments' efficacy was achieved through the study of the parasitological and pathological investigations. Results: C. molmol extract and Cm-CsNFs (100 mg/kg) significantly reduced the mean number of intestinal oocysts. All the treatment regimens significantly (P<0.05) diminished the mean number of trophozoites relative to the infected non-treated group. Apparent pathological alterations in intestinal tissues were consistent with cryptosporidiosis. Cryptosporidium-induced pathological alterations were improved remarkably in Cm-CsNFs (100 mg/kg) group. Conclusion: Our study highlights the efficacy of C. molmol as an extract or conjugated with CsNFs in declining cryptosporidiosis and attenuating Cryptosporidium-induced intestinal injury.

Background: In trichinosis, current treatments (Albendazole), have limited efficiency in eliminating encapsulated larvae because of poor absorption and potential side effects at higher doses. Objective: The therapeutic efficacy of allopurinol, given alone or in combination with albendazole, was investigated in a murine model of trichinosis. Material and Methods: Eighty male albino mice were assigned to two experimental phases: an intestinal phase-experiment I and a muscle phase-experiment II. In each experiment, mice were divided into four treatment groups: infected untreated administered normal saline, treated with albendazole (50 mg/kg), allopurinol (30 mg/kg), and combined treatment. In both experiments, efficacy was evaluated on the 6th, and 45th day of infection, respectively. Parameters used were parasite burden in the intestine and in 3 muscle tissues (tongue, diaphragm, and thoracic muscle), and histopathological examinations. Results: Albendazole alone reduced intestinal worm burden by 98.53% with limited effect on muscle larvae (56.86-59.44% reduction). Allopurinol showed moderate effect on intestinal worms (69.41% reduction) but with higher efficacy against muscle larvae (83.93-87.36% reduction). The combined therapy was the most effective, reducing intestinal worms by 98.76%, and muscle larvae by 93.69-94.71%. Histopathological examination showed that the combined treatment minimized inflammation and myodestruction with extensive larval degeneration. Conclusion: Allopurinol showed higher efficiency against muscular trichinosis, while albendazole was more effective against intestinal trichinosis. The combination therapy yielded promising results against both phases, suggesting potential as an improved treatment for trichinosis. Further clinical investigations are recommended to confirm these findings.

Artificial intelligence (AI) possesses the ability to transform diabetic foot care through enhancing the early identification, categorization, and management of diabetic foot ulcers and gangrene. Incorporation of multimodal sensors, privacy-preserving federated learning, and hybrid convolutional neural network–support vector machine frameworks are some of the AI-driven approaches synthesized in this narrative review. Improving diagnostic accuracy, real-time assessment of risks, and potentially scalable, cost-effective implementation within both urban and rural areas are all included in the clinical potential of AI technology, which the literature review highlighted. Still, there are numerous obstacles to overcome—including a lack of data, inconsistent annotations, outdated technology, biased algorithms—and the need for algorithms that are both explainable and ethically acceptable. In order to cross the gap between breakthrough technology and real-world clinical significance, collaborative attempts that involve standard protocols, compliance with regulations, health care provider commitment, and multidisciplinary teamwork are needed. By addressing these issues and promoting ethical, inclusive, and transparent AI applications, the field has the opportunity to advance in the direction of fair and reliable diabetic foot care, subsequently reducing morbidity and improving patient outcomes on a global basis.