Urinary tract infections (UTIs) are the most frequent bacterial infections in the clinical setting. Even without underlying anatomic or functional abnormalities, more than 40% of women experience at least one UTI in their lifetime, of which 30% develop recurrent UTIs (rUTIs) within 6 months. Conventional management with antibiotics for rUTIs may eventually lead to the development of multidrug-resistant uropathogens. Targeting of the pathogenicity of rUTIs, the evolution of uropathogenic Escherichia coli (UPEC), and inadequate host defenses by immune responses should be explored to provide non-antibiotic solutions for the management of rUTIs. The adaptive evolution of UPEC has been observed in several aspects, including colonization, attachment, invasion, and intracellular replication to invade the urothelium and survive intracellularly. Focusing on the antivirulence of UPEC and modulating the immunity of susceptible persons, researchers have provided potential alternative solutions in four categories: antiadhesive treatments (i.e., cranberries and D-mannose), immunomodulation therapies, vaccines, and prophylaxis with topical estrogen therapy and probiotics (e.g., Lactobacillus species). Combination therapies targeting multiple pathogenic mechanisms are expected to be a future trend in UTI management, although some of these treatment options have not been well established in terms of their long-term efficacy. Additional clinical trials are warranted to validate the therapeutic efficacy and durability of these techniques.

Neurogenic lower urinary tract dysfunction, common in patients with chronic spinal cord injury, inevitably results in urological complications. To address neurogenic lower urinary tract dysfunction after spinal cord injury, proper and adequate bladder management is important in spinal cord injury rehabilitation, with the goal and priorities of the protection of upper urinary tract function, maintaining continence, preserving lower urinary tract function, improvement of SCI patients’ quality of life, achieving compatibility with patients’ lifestyles, and decreasing urological complications. This concise review aims to help urologists address neurogenic lower urinary tract dysfunction by focusing on the risks of long-term urological complications and the effects of different bladder management strategies on these complications based on scientifically supported knowledge.

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.

Sensory bladder disorders encompass several distinct conditions with overlapping symptoms, which pose diagnostic challenges. This study aimed to evaluate urine biomarkers for differentiating between various sensory bladder disorders, including non-Hunner’s interstitial cystitis (NHIC), detrusor overactivity (DO), hypersensitive bladder (HSB), and urodynamically normal women. A retrospective analysis of 191 women who underwent a videourodynamic study (VUDS) was conducted, with some also receiving cystoscopic hydrodistention to confirm the presence of NHIC. Participants were categorized into four groups: DO (n = 51), HSB (n = 29), NHIC (n = 81), and normal controls (n = 30). The urine levels of inflammatory and oxidative stress biomarkers were measured. The DO patients exhibited elevated IP-10 levels, while the HSB patients had decreased TAC and 8-OHdG levels. The NHIC patients showed lower IL-2 and higher TNF-α levels. A TNF-α ≥ 1.05 effectively identified NHIC, with an AUROC of 0.889, a sensitivity of 98.8%, and a specificity of 81.3%. An IP-10 ≥ 6.31 differentiated DO with an AUROC of 0.695, a sensitivity of 56.8%, and a specificity of 72.3%. An 8-OHdG ≤ 14.705 and a TAC ≤ 528.7 identified HSB with AUROCs of 0.754 and 0.844, respectively. The combination of 8-OHdG and TAC provided an AUROC of 0.853 for HSB. These findings suggest that TNF-α, IP-10, TAC, 8-OHdG, and IL-2 are promising non-invasive biomarkers for distinguishing between these conditions, which may improve diagnosis and management.