Investigating the impact of modified mindfulness-based stress reduction (MBSR) program on occupational burnout and other mental health status among nonmedical staff in a hospital: a randomized controlled trial.

OBJECTIVE: Burnout is a prolonged psychological response to a longstanding interpersonal stressor at work. It can progress to other mental illnesses, such as anxiety and depression. In today's society, burnout has become widespread, and it is currently a serious challenge in health systems. This study intended to investigate the impact of mindfulness training on burnout and depression, anxiety, and stress of nonmedical staff in a hospital in Shiraz-Iran. METHOD: Fifty nonmedical staff in a Shiraz-Iran hospital were enrolled in this two groups' randomized controlled trial. The intervention group was trained by a modified mindfulness-based stress reduction (MBSR) program, and the control group received no intervention. The outcome was assessed by the Copenhagen burnout inventory (CBI) and DASS-21 questionnaire on three occasions including before T0, immediately after T1, and 3 months after the intervention T2. RESULTS: Comparing the score changes between intervention and control groups showed that the reduction of work­distaste­related burnout, client­related burnout, anxiety, and stress scores in the intervention group was significantly more than in the control group in the time interval from T0 to T1. The scores in the intervention group in three subscales of CBI, including work­characteristic­related burnout, work­distaste­related burnout, and client­related burnout, decreased significantly immediately after the training (T1) compared to before (T0). Also, the scores did not change significantly after 3 months following training. CONCLUSIONS: Mindfulness training can be used as an effective way to improve occupational burnout, anxiety, and stress in occupations other than health professionals. Its effect is stable for at least a few months. It is recommended that future studies focus on investigating the feasible way to integrate this training into the working environment. Forthcoming studies should also determine whether the impact of this intervention will last longer and find possible ways to extend its influence.

A case report of COVID-19 in a patient with non-Hodgkin's lymphoma.

BACKGROUND: The current literature is scarce as to the outcomes of COVID-19 infection in non-Hodgkin's lymphoma patients and whether immunosuppressive or chemotherapeutic agents can cause worsening of the patients' condition during COVID-19 infection. CASE PRESENTATION: Our case is a 59-year-old gentleman who presented to the Emergency Department of the Cancer Institute of Hospital das Clínicas da Universidade de São Paulo, São Paulo, Brazil on 10th May 2020 with a worsening dyspnea and chest pain which had started 3 days prior to presentation to the Emergency Department. He had a past history of non-Hodgkin's lymphoma for which he was receiving chemotherapy. Subsequent PCR testing demonstrated that our patient was SARS-CoV-2 positive. CONCLUSION: In this report, we show a patient with non-Hodgkin lymphoma in the middle of chemotherapy, presented a mild clinical course of COVID-19 infection.

Amyloidosis cutis dyschromica: report of 3 cases.

Amyloidosis cutis dyschromica (ACD) is a rare pigmentary disorder with about 50 cases having been reported in the English literature. Only one case of ACD has been reported from Iran. We present three patients who presented with generalized hyper- and hypopigmented patches, sparing face, hands, and feet in all three cases. The presence of amorphous eosinophilic deposits in the papillary dermis confirmed the diagnosis of ACD; the deposits were stained by crystal violet in the histopathological examination of the lesions. In all three cases, similar lesions were present in some of the family members. ACD should be considered in the differential diagnosis of diffuse hyperpigmentation studded with hypopigmentation, especially when beginning in childhood.

Comparing the effects of SMS-based education with group-based education and control group on diabetes management: a randomized educational program.

BACKGROUND: The high prevalence of diabetes and the importance of long-term follow-up of these patients encourage finding an inexpensive and applicable educational method to control the disease. Distance education based on mobile technology and Short message service (SMS) can be an effective way to manage this disease by eliminating time and place limitations. Due to the world's high penetration rate, SMS is one of the best ways to transfer information and health education. OBJECTIVE: This study aimed to compare the effect of SMS- and group-based education in managing diabetes type 2 and compare them with a control group. METHOD: A total of 168 patients with diabetes type 2 under the coverage of three family physician clinics were randomly allocated into three groups. The education was conducted in 12 one-hour sessions once a week in the group-based arm, and a daily short message was sent to the participants in the SMS group. The control group also underwent routine care at the family physician clinic. The duration of the education was 3 months. At baseline and 3 months later, fasting blood sugar (FBS), 2 hours postprandial sugar (2hppBS), and HBA1c, as well as diabetes self-management questionnaire score (DSMQ), were measured. RESULTS: The comparison of the three groups in terms of changes in FBS (P-value: 0.001), 2hppBS (8 P-value: < 0.001) and HbA1c (P-value: < 0.001) were significantly different after 3 months. In pairwise analysis, 2hppBS was the only significantly different parameter between the group- and SMS-based education (P-value: 0.035). CONCLUSION: Although the effect of both educational methods via SMS or group education was better than the control group in controlling diabetes, these two methods were not statistically different. Due to spending a lot of time and money on group-based education, it is better to replace it with education by SMS.

A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.

Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells.

BACKGROUND: Morphologic rare cell detection is a laborious, operator-dependent process which has the potential to be improved by the use of image analysis using artificial intelligence. Detection of rare hemoglobin H (HbH) inclusions in red cells in the peripheral blood is a common screening method for alpha-thalassemia. This study aims to develop a convolutional neural network-based algorithm for the detection of HbH inclusions. METHODS: Digital images of HbH-positive and HbH-negative blood smears were used to train and test the software. The software performance was tested on images obtained at various magnifications and on different scanning platforms. Another model was developed for total red cell counting and was used to confirm HbH cell frequency in alpha-thalassemia trait. The threshold minimum red cells to image for analysis was determined by Poisson modeling and validated on image sets. RESULTS: The sensitivity and specificity of the software for HbH+ cells on images obtained at ×100, ×60, and ×40 objectives were close to 91% and 99%, respectively. When an AI-aided diagnostic model was tested on a pilot of 40 whole slide images (WSIs), good inter-rater reliability and high sensitivity and specificity of slide-level classification were obtained. Using the lowest frequency of HbH+ cells (1 in 100,000) observed in our study, we estimated that a minimum of 2.4 × 106 red cells would need to be analyzed to reduce misclassification at the slide level. The minimum required smear size was validated on 78 image sets which confirmed its validity. CONCLUSIONS: WSI image analysis can be utilized effectively for morphologic rare cell detection. The software can be further developed on WISs and evaluated in future clinical validation studies comparing AI-aided diagnosis with the routine diagnostic method.