Urinary tract infections (UTIs) are a common indication for antibiotic use among adult outpatients in low- and middle-income countries, yet data on community-acquired UTIs (CA-UTIs) and antimicrobial resistance at primary healthcare facilities remain limited. A facility-based cross-sectional study was conducted from May to August 2024 at Igoma and Buzuruga Health Centres in Mwanza, Tanzania. Symptomatic adults without hospitalization within the preceding 30 days or urinary catheterization were enrolled. Clinical and demographic data were collected, and midstream urine specimens were cultured, followed by antimicrobial susceptibility testing using the disc diffusion method. Of 1,005 participants, the median age was 32 years [IQR: 23-49], 727 (72.3%) were female, and 64.3% reported to have been diagnosed with UTI within the previous six months. The median symptom duration before presentation was 8 days [IQR: 5-15]. Culture-confirmed UTI was detected in 221 participants (22.0%; 95% CI: 19.5-24.7), with Escherichia coli (32.9%) and Staphylococcus aureus (24.7%) being the most frequently isolated pathogens. Only 35.7% of culture-confirmed cases are presented with classic urinary symptoms of dysuria, frequency, or urgency. E. coli showed high resistance to ampicillin (93.4%), ciprofloxacin (62.3%), trimethoprim-sulfamethoxazole (60.7%), amoxicillin-clavulanic acid (57.4%), and tetracycline (55.7%); 82.8% were multidrug-resistant and 20.3% exhibited extended-spectrum beta-lactamase phenotypes. Among S. aureus isolates, 64.6% were cefoxitin-resistant, indicating methicillin resistance. CA-UTIs therefore constitutes a substantial burden among adult primary-care outpatients, accompanied by high antimicrobial resistance and frequent atypical clinical presentations. Improved access to UTI diagnostics, strengthened antimicrobial stewardship, and locally informed treatment guidelines are needed, particularly given the high resistance to commonly used empirical antibiotics.

The Weiss nocturia indices have structured clinical thinking about nocturia for over 2 decades. By deriving simple ratios from the frequency-volume chart, they classify nocturia into nocturnal polyuria, reduced nocturnal bladder capacity, and mixed etiology. The arithmetic is sound. But the framework rests on an unexamined assumption: that the maximum voided volume, almost always the first morning void, represents "bladder capacity," and that the gap between this value and daytime voided volumes reflects normal physiology. We propose that what the frequency-volume chart measures during wakefulness is not capacity but a personal adaptive afferent threshold-a neurally determined set-point at which the individual initiates voiding, always below the structural capacity the detrusor permits. The first morning void approximates true structural capacity, revealed when sleep suppresses the cortical circuits that enforce the waking threshold. This reframing unifies the entire overactive bladder spectrum-normal voiding, frequency, urgency, urge incontinence-as positions on a single threshold continuum. It reveals nocturia from "reduced nocturnal bladder capacity" as the intrusion of the waking threshold into sleep when central afferent gating fails. The Weiss parameters captured the output of this system with numerical precision but encoded the threshold artifact as the reference norm, directing a generation of research toward the bladder in patients whose problem may be the brain.

Interstitial cystitis (IC) and overactive bladder (OAB) are chronic pelvic conditions with shared symptoms of urinary urgency, increased frequency, and nocturia with pain in the bladder. IC is characterized by bladder pain and inflammation without a clear etiology, whereas OAB involves detrusor overactivity in the absence of infection. Despite this symptomatic overlap, IC and OAB differ in underlying pathophysiology and require distinct treatment and medical care. A specific diagnostic strategy is currently lacking and highlights the need of a robust diagnostic method for effective treatment. Raman spectroscopy provides sensitive and nondestructive molecular fingerprints; when combined with artificial intelligence (AI)-driven analysis of spectral data, it offers a promising approach to overcoming the limitations of exclusion-based diagnoses. In this study, urinary samples were collected from healthy individuals (n = 117), IC patients (n = 19), and OAB patients (n = 45) for the acquisition of Raman spectra using Au-ZnO nanorod surface-enhanced Raman spectroscopy (SERS) chips. To obtain biological interpretable Raman spectra per group and high accuracy of classification performance, the linear models PCA-PLS-DA and PCA-LDA and tree-based nonlinear models XGBoost and LightGBM were applied and reached up to 92% accuracy with interpretable and pathologic relative Raman features for discriminating among healthy control, IC, and OAB. This approach suggests that urine-based SERS Raman spectra combined with machine learning could serve as a promising diagnostic platform to support disease-specific clinical decision.