Nanotechnology Involved in Treating Urinary Tract Infections: An Overview

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

BACKGROUND

Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

METHODS

The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.

RESULTS

For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort).

CONCLUSION

We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Relationship between urinary tract infections and serum vitamin D level in adults and children- a literature review

Over time, researchers have accumulated significant evidence indicating that vitamin D deficiency not only impacts skeletal health but also contributes to the development and progression of various diseases, including cancer, diabetes, and cardiovascular conditions. The risk of low serum 1, 25(OH)2D3 level ultimately directs the way to morbidity, the beginning of new diseases, and numerous infections. Infections are the first entity that affects those with vitamin D deficiency. The common infection is urinary tract infection (UTI), and its relationship with vitamin D deficiency or insufficiency remains controversial. This infection affects both men and women, but comparatively, women are more prone to this infection because of the short length of the urethra, which makes an easy entry for the bacteria. The low level of serum vitamin D increases the risk of UTIs in children. Recurrent UTIs are one of the major weaknesses in women; if left untreated, they progress to appallingly serious conditions like kidney dysfunction, liver damage, etc. Hence improving the vitamin D status may help to improve the immune system, thus making it more resistant to infections. In this review, we have focused on examining whether vitamin D deficiency and insufficiency are the causes of UTIs and the association between them in women and children. We have also described the connection between vitamin D deficiency and insufficiency with UTIs and additional nanotechnology- based treatment strategies.

Novel Antimicrobial Approaches to Combat Bacterial Biofilms Associated with Urinary Tract Infections

Urinary tract infections (UTIs) are prevalent bacterial infections in both community and healthcare settings. They account for approximately 40% of all bacterial infections and require around 15% of all antibiotic prescriptions. Although antibiotics have traditionally been used to treat UTIs for several decades, the significant increase in antibiotic resistance in recent years has made many previously effective treatments ineffective. Biofilm on medical equipment in healthcare settings creates a reservoir of pathogens that can easily be transmitted to patients. Urinary catheter infections are frequently observed in hospitals and are caused by microbes that form a biofilm after a catheter is inserted into the bladder. Managing infections caused by biofilms is challenging due to the emergence of antibiotic resistance. Biofilms enable pathogens to evade the host's innate immune defences, resulting in long-term persistence. The incidence of sepsis caused by UTIs that have spread to the bloodstream is increasing, and drug-resistant infections may be even more prevalent. While the availability of upcoming tests to identify the bacterial cause of infection and its resistance spectrum is critical, it alone will not solve the problem; innovative treatment approaches are also needed. This review analyses the main characteristics of biofilm formation and drug resistance in recurrent uropathogen-induced UTIs. The importance of innovative and alternative therapies for combatting biofilm-caused UTI is emphasised.

New Insights into the Pathogenesis and Treatment of Urinary Tract Infections

About 150 million people around the world experience urinary tract infections (UTI) every year, with adult women 30 times more likely to develop a UTI than men [...].