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According to the World Health Organization (WHO) estimates, there were 558,000 new cases of rifampicin-resistant tuberculosis, with 82% of these classified as multidrug-resistant tuberculosis (MDR-TB). This study aimed to investigate the outbreak of MDR-TB in River Nile State, Sudan, and identify the risk factors associated with its occurrence. A descriptive cross-sectional hospital-based study was conducted involving two hundred specimens from patients suspected of having MDR-TB, tested using the automated GeneXpert assay. The GeneXpert results indicated that Mycobacterium tuberculosis was detected in 81 cases (40.5%), and among these positive results, 13 (16%) were confirmed as MDR-TB. Furthermore, 7 of the MDR-TB cases had a history of previous treatment, accounting for approximately 53 percent of MDR patients. In contrast, the other 6 MDR-TB cases were new, representing 47% of the MDR-TB patients. In addition, 4 MDR-TB patients had a history of contact with other MDR-TB cases. The prevalence of MDR-TB in River Nile State, Sudan, was found to be 16%, which is higher than the WHO estimate for Sudan at 10.1%. The findings highlighted that prior contact with MDR-TB patients is the primary risk factor for developing MDR-TB, emphasizing that treatment adherence and increased social awareness about MDR-TB transmission are essential preventive measures.

Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, is the most severe form of motor neuron degeneration. This study aims to: (1) compare genetic and non-genetic contributors to ALS development, (2) evaluate the pharmacological mechanisms of riluzole and its therapeutic potential across multiple conditions, and (3) explore treatment combinations for managing symptoms throughout ALS progression. The analysis was conducted using data from established electronic medical databases. The most frequently implicated genetic mutations in ALS include SOD1, SETX, FUS, VEGF, VAPB, ANG, TARDBP, FIG4, OPTN, ATXN2, VCP, UBQLN2, SIGMAR1, CHMP2B, PFN1, ERBB4, HNRNPA1, C9orf72, dynactin 1, H46R, and A4V. Additional risk factors include oxidative stress, glutamate-induced excitotoxicity, autoimmune responses, protein misfolding and aggregation, inflammation, and viral infections. Riluzole’s therapeutic actions are attributed to several mechanisms: (1) inhibition of repetitive neuronal firing, (2) blockade of persistent sodium currents in motor neurons, (3) enhancement of calcium-activated potassium currents, (4) reduction of presynaptic neurotransmitter release, and (5) attenuation of postsynaptic receptor responses. Combining riluzole with antioxidants such as vitamins E and C, coenzyme Q10, creatine, and selenium may enhance therapeutic efficacy in ALS. Symptomatic treatments include nonsteroidal anti-inflammatory drugs, opioids for pain relief, and agents like Baclofen and Dantrolene to manage spasticity. Memantine, Nimesulide, and Gabapentin show promise for further research. Due to its diverse mechanisms, riluzole is also being investigated for use in Parkinson’s disease, Huntington’s disease, Machado-Joseph disease, multiple sclerosis, spinal muscular atrophy, and various neuropsychiatric conditions, including anxiety, autism, depression, and schizophrenia.

The objective of the current study was to investigate the role of the MDH enzyme system in liver cells of alloxan-induced diabetic rats and to examine how the administration of inulin from Jerusalem artichoke affects the enzyme’s activity and gene transcription in these diabetic rats’ livers. In this study, male Wistar rats weighing 150-200 g were selected for the experiment. Diabetes mellitus was induced by a single intraperitoneal injection of 5% alloxan monohydrate (in 0.9% saline). The control group received an equivalent amount of saline solution. Statistical analysis was performed using StatTech v. 1.2.0 software. An increase in NAD-dependent malate dehydrogenase (MDH) activity, accompanied by the emergence of a novel liver isoform, was observed in rats with alloxan-induced diabetes. This finding suggests the potential involvement of the malate dehydrogenase enzyme system in the body’s adaptive response to oxidative stress induced by biochemical changes in diabetic cells. In type I diabetes, this rise in enzyme activity is associated with the appearance of an additional MDH isoform in peroxisomes. Gene expression analysis of mdh1 and mdh2 indicates that diabetes triggers enzyme activation at the gene transcription level. When inulin was administered, it significantly reduced blood glucose levels in rats with alloxan-induced diabetes and restored the regular transcriptional activity of these genes. Consequently, the formation of the new MDH isoform was prevented. This suggests that inulin could be a promising option for pharmacologically managing the metabolic changes associated with diabetes-related pathologies.

Generative artificial intelligence is becoming increasingly relevant to pharmaceutical formulation because it can propose compositions, excipient combinations, processing conditions, and optimisation trajectories that may not be obvious through conventional experimental design. These capabilities create the possibility of faster development, broader exploration of formulation space, and more systematic use of prior knowledge. Yet the same models that expand formulation creativity often operate through complex latent representations that are difficult to interpret. This creates a trust problem for both scientific and regulatory decision-making. The central problem is that an AI-generated formulation is not only a predicted technical solution but also a claim about product performance, manufacturability, and quality. If the rationale behind that claim cannot be explained, formulation scientists may struggle to convert model outputs into mechanistic understanding. Regulators may likewise find it difficult to assess whether the proposed formulation is supported by transparent evidence. Opaque formulation design therefore risks becoming a translational bottleneck rather than an innovation accelerator. This perspective develops a conceptual framework for dual-purpose explainability in AI-generated pharmaceutical formulation design. The framework is designed to serve two decision contexts simultaneously. Scientific decision-makers require explanations that clarify formulation logic, reveal influential variables, and support hypothesis generation. Regulatory decision-makers require explanations that are auditable, reproducible, uncertainty-aware, and connected to product quality and safety evidence. The article first defines the conceptual gap between existing AI formulation capabilities and explainability expectations. It then describes the logic of AI-generated formulation, identifies distinct scientific and regulatory explanation requirements, and analyses transparency barriers. The proposed framework integrates global model explanations, local formulation-specific explanations, mechanistic interpretation, uncertainty communication, and regulatory evidence packaging. Three tables summarise the gap analysis, explainability requirements, and framework architecture. The article concludes that explainability must be treated as a design requirement rather than a post hoc add-on to pharmaceutical AI. AI-generated formulation designs will become useful only when their rationale can be interrogated, documented, challenged, and connected to established principles of product and process understanding. A dual-purpose explainability framework can help move the field from black-box prediction toward transparent, accountable, and scientifically meaningful formulation intelligence.