Patient self-referral patterns in a developing country: characteristics, prevalence, and predictors.

BACKGROUND: Efficient healthcare delivery and access to specialized care rely heavily on a well-established healthcare sector referral system. However, the referral system faces significant challenges in developing nations like Bangladesh. This study aimed to assess self-referral prevalence among patients attending tertiary care hospitals in Bangladesh and identify the associated factors. METHODS: This cross-sectional study was conducted at two tertiary care hospital, involving 822 patients visiting their outpatient or inpatient departments. A semi-structured questionnaire was used for data collection. The patients' mode of referral (self-referral or institutional referral) was considered the outcome variable. RESULTS: Approximately 58% of the participants were unaware of the referral system. Of all, 59% (485 out of 822) of patients visiting tertiary care hospitals were self-referred, while 41% were referred by other healthcare facilities. The primary reasons for self-referral were inadequate treatment (28%), inadequate facilities (23%), critical cases (14%), and lack of expert physicians (8%). In contrast, institutional referrals were mainly attributed to inadequate facilities to treat the patient (53%), inadequate treatment (47%), difficult-to-treat cases (44%), and lack of expert physicians (31%) at the time of referral. The private facilities received a higher proportion of self-referred patients compared to government hospitals (68% vs. 56%, p < 0.001). Among patients attending the study sites through institutional referral, approximately 10% were referred from community clinics, 6% from union sub-centers, 25% from upazila health complexes, 22% from district hospitals, 22% from other tertiary care hospitals, and 42% from private clinics. Patients visiting the outpatient department (adjusted odds ratio [aOR] 3.3, 95% confidence interval [CI] 2.28-4.82, p < 0.001), residing in urban areas (aOR 1.29, 95% CI 1.04-1.64, p = 0.007), belonging to middle- and high-income families (aOR 1.34, 95% CI 1.03-1.62, p = 0.014, and aOR 1.98, 95% CI 1.54-2.46, p = 0.005, respectively), and living within 20 km of healthcare facilities (aOR 3.15, 95% CI 2.24-4.44, p-value < 0.001) exhibited a higher tendency for self-referral to tertiary care facilities. CONCLUSIONS: A considerable number of patients in Bangladesh, particularly those from affluent urban areas and proximity to healthcare facilities, tend to self-refer to tertiary care centers. Inadequacy of facilities in primary care centers significantly influences patients to opt for self-referral.

Deep learning based object detection and surrounding environment description for visually impaired people.

Object detection, one of the most significant contributions of computer vision and machine learning, plays an immense role in identifying and locating objects in an image or a video. We recognize distinct objects and precisely get their information through object detection, such as their size, shape, and location. This paper developed a low-cost assistive system of obstacle detection and the surrounding environment depiction to help blind people using deep learning techniques. TensorFlow object detection API and SSDLite MobileNetV2 have been used to create the proposed object detection model. The pre-trained SSDLite MobileNetV2 model is trained on the COCO dataset, with almost 328,000 images of 90 different objects. The gradient particle swarm optimization (PSO) technique has been used in this work to optimize the final layers and their corresponding hyperparameters of the MobileNetV2 model. Next, we used the Google text-to-speech module, PyAudio, playsound, and speech recognition to generate the audio feedback of the detected objects. A Raspberry Pi camera captures real-time video where real-time object detection is done frame by frame with Raspberry Pi 4B microcontroller. The proposed device is integrated into a head cap, which will help visually impaired people to detect obstacles in their path, as it is more efficient than a traditional white cane. Apart from this detection model, we trained a secondary computer vision model and named it the "ambiance mode." In this mode, the last three convolutional layers of SSDLite MobileNetV2 are trained through transfer learning on a weather dataset. The dataset comprises around 500 images from four classes: cloudy, rainy, foggy, and sunrise. In this mode, the proposed system will narrate the surrounding scene elaborately, almost like a human describing a landscape or a beautiful sunset to a visually impaired person. The performance of the object detection and ambiance description modes are tested and evaluated in a desktop computer and Raspberry Pi embedded system. Detection accuracy and mean average precision, frame rate, confusion matrix, and ROC curve measure the model's accuracy on both setups. This low-cost proposed system is believed to help visually impaired people in their day-to-day life.

The antioxidant effect of astaxanthin is higher in young mice than aged: a region specific study on brain.

Astaxanthin is a potential antioxidant which shows neuroprotective property. We aimed to investigate the age-dependent and region-specific antioxidant effects of astaxanthin in mice brain. Animals were divided into 4 groups; treatment young (3 months, n = 6) (AY), treatment old (16 months, n = 6) (AO), placebo young (3 months, n = 6) (PY) and placebo old (16 months, n = 6) (PO) groups. Treatment group was given astaxanthin (2 mg/kg/day, body weight), and placebo group was given 100 µl of 0.9% normal saline orally to the healthy Swiss albino mice for 4 weeks. The level of non-enzymatic oxidative markers namely malondialdehyde (MDA); nitric oxide (NO); advanced protein oxidation product (APOP); glutathione (GSH) and the activity of enzymatic antioxidants i.e.; catalase (CAT) and superoxide dismutase (SOD) were determined from the isolated brain regions. Treatment with astaxanthin significantly (p < 0.05) reduces the level of MDA, APOP, NO in the cortex, striatum, hypothalamus, hippocampus and cerebellum in both age groups. Astaxanthin markedly (p < 0.05) enhances the activity of CAT and SOD enzymes while improves the level of GSH in the brain. Overall, improvement of oxidative markers was significantly greater in the young group than the aged animal. In conclusion, we report that the activity of astaxanthin is age-dependent, higher in young in compared to the aged brain.