Recent studies have shown that orexins play a critical role in the regulation of sleep/wake states, feeding behaviour, and reward processes. The exocrine and endocrine pancreas are involved in the regulation of food metabolism and energy balance. This function is deranged in diabetes mellitus. This study examined the pattern of distribution of orexin-1 receptor (OX1R) in the endocrine cells of the pancreas of normal and diabetic Wistar (a model of type 1 diabetes), Goto-Kakizaki (GK, a model of type 2 diabetes) rats and in orexin-deficient (OX−/−) and wild type mice. Diabetes mellitus (DM) was induced in Wistar rats and mice by streptozotocin (STZ). At different time points (12 h, 24 h, 4 weeks, 8 months and 15 months) after the induction of DM, pancreatic fragments of normal and diabetic rats were processed for immunohistochemistry and Western blotting. OX1R-immunoreactive nerves were observed in the pancreas of normal and diabetic Wistar rats. OX1R was also discernible in the pancreatic islets of normal and diabetic Wistar and GK rats, and wild type mice. OX1R co-localized with insulin (INS) and glucagon (GLU) in the pancreas of Wistar and GK rats. The number of OX1R-positive cells in the islets increased markedly (p<0.0001) after the onset of DM. The increase in the number of OX1R-positive cells is associated with a high degree of co-localization with GLU. The number of GLU- positive cells expressing OX1R was significantly (p<0.0001) higher after the onset of DM. The tissue level of OX1R protein increased with the duration of DM especially in type 1 diabetes where it co-localized with cleaved caspase 3 in islet cells. In comparison to STZ-treated wild type mice, STZ-treated OX−/− animals exhibited reduced hyperglycemia and handled glucose more efficiently in glucose tolerance test. The findings suggest an important role for the OX-OX1R pathway in STZ-induced experimental diabetes.

Prostate cancer rarely metastasis to the rectum. Findings in the patient reported here emphasize the importance of the relationship between urinary and gastrointestinal symptoms in detecting prostatic neoplasms in older male patients.

Hypospadias, characterized by misplacement of the urinary meatus in the lower side of the penis, is a frequent birth defect in male children. Because of the huge variation in the anatomic presentation of hypospadias, no single urethroplasty procedure is suitable for all situations. Hence, many surgical techniques have emerged to address the shortage of tissues required to bridge the gap in the urethra particularly in the severe forms of hypospadias. However, the rate of postoperative complications of currently available surgical procedures reaches up to one-fourth of the patients having severe hypospadias. Moreover, these urethroplasty techniques are technically demanding and require considerable surgical experience. These limitations have fueled the development of novel tissue engineering techniques that aim to simplify the surgical procedures and to reduce the rate of complications. Several types of biomaterials have been considered for urethral repair, including synthetic and natural polymers, which in some cases have been seeded with cells prior to implantation. These methods have been tested in preclinical and clinical studies, with variable degrees of success. This review describes the different urethral tissue engineering methodologies, with focus on the approaches used for the treatment of hypospadias. At present, despite many significant advances, the search for a suitable tissue engineering approach for use in routine clinical applications continues.

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PurposeTesticular torsion (TT) represents a clinical challenge that needs emergency surgical assessment. It is common to have negative scrotal exploration due to confounding symptoms and signs which makes it sometimes difficult to differentiate from similar surgical emergencies that do not warrant surgery. At the same time, several occasions of misdiagnoses or late interventions occur with devastating effects. We aim at delineating the significance of the different clinical, laboratory, and radiological variables in the detection of TT.MethodsWe retrospectively reviewed the charts of 52 patients who were surgically explored with a preoperative working diagnosis of suspected TT in our center over the period from 2011 to 2015. All the patients were examined by pediatric surgeons in the emergency room and had undergone ultrasound imaging of the testes. The ultrasound images were retrospectively reviewed by a pediatric radiologist who was blinded to the intraoperative findings. Univariate and multivariate and logistic regression analyses were performed.ResultsOf the studied group of patients, the majority (84.6%) had TT upon surgical exploration. The most frequently presented symptom was pain (80.8%), and only a minority (11.5%) presented with vomiting. Radiological findings with the highest sensitivity were heterogeneous echogenicity in favor of TT and enlarged epididymis indicating that TT is unlikely. However, the predictability of TT by any of the assessed clinical and imaging factors was statistically insignificant.ConclusionIt is important to gather all relevant data from clinical, laboratory, and imaging sources when assessing pediatric patients with suspected TT given the inaccuracy of each single one of them if used alone. Keeping this in mind, Doppler ultrasound has a significant role to aid in the accuracy of the diagnosis and hence the appropriate decision-making thereafter. However, we found no single clinical or imaging sign that is sensitive enough to prove or rule out TT. Therefore, surgical exploration should take place in a timely manner. Moreover, further research is necessary to construct scoring systems where different predictors collectively have higher reliability.

Several congenital and acquired conditions may result in severe narrowing of the urethra in men, which represent an ongoing surgical challenge and a significant burden on both health and quality of life. In the field of urethral reconstruction, tissue engineering has emerged as a promising alternative to overcome some of the limitations associated with autologous tissue grafts. In this direction, preclinical as well as clinical studies, have shown that degradable scaffolds are able to restore the normal urethral architecture, supporting neo-vascularization and stratification of the tissue. While a wide variety of degradable biomaterials are under scrutiny, such as decellularized matrices, natural, and synthetic polymers, the search for scaffold materials that could fulfill the clinical performance requirements continues. In this article, we discuss the design requirements of the scaffold that appear to be crucial to better resemble the structural, physical, and biological properties of the native urethra and are expected to support an adequate recovery of the urethral function. In this context, we review the biological performance of the degradable polymers currently applied for urethral reconstruction and outline the perspectives on novel functional polymers, which could find application in the design of customized urethral constructs.

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Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.