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We report a case of 42-year-old male presented with acute onset breathlessness and blurring of vision in both eyes. Upon evaluation, the patient is found to have vitamin B12 and iron deficiency anemia; ophthalmoscopic examination revealed bilateral preretinal hemorrhages and Roth spots. This case documents the occurrence of retinopathy in nutritional dimorphic anemia causing blindness which is reversible after correction of anemia.

Background: There is dearth of studies related to pattern of disability among persons who availed psychosocial rehabilitation services in India. We studied the pattern of disability among persons who availed half-way home-care services for psychosocial rehabilitation. Materials and Methods: Out of 130 case files of discharged patients, 50 files were randomly selected for data collection. Indian Disability Evaluation and Assessment Schedule was used to assess the pattern of disability in the sample. Results: The study revealed that only one-third (35%) of the residents had disability in self-care, 41% in communication and understanding and 47% in interpersonal relationship. Overall, majority (76%) of the respondents had moderate level of psychiatric disability at the time of discharge from half-way home. There was no significant relationship between gender and type of psychiatric illness with the level of disability. The overall disability correlated positively with the duration of illness (rs=0.39). Conclusion: Three-fourth of the residents who availed half-way home-care services had moderate level of disability.

Rationale and Objectives: Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies., Materials and Methods: The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processing and descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL., Results: Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65 seconds; p < 0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p < 0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p < 0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL., Conclusion: A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Halid Ziya Yerebakan reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. Yoshihisa Shinagawa reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. Gerardo Hermosillo reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. Simon Allen-Raffl reports a relationship with Siemens Medical Solutions USA Inc that includes: employment., (Copyright © 2023. Published by Elsevier Inc.)

Premise: The consequences of acidity for plant performance are profound, yet the prevalence and causes of low pH in bromeliad tank water are unknown despite its functional relevance to key members of many neotropical plant communities., Methods: We investigated tank water pH for eight bromeliad species in the field and for the widely occurring Guzmania monostachia in varying light. We compared pH changes over time between plant and artificial tanks containing a solution combined from several plants. Aquaporin transcripts were measured for field plants at two levels of pH. We investigated relationships between pH, leaf hydraulic conductance, and CO 2 concentration in greenhouse plants and tested proton pump activity using a stimulator and inhibitor., Results: Mean tank water pH for the eight species was 4.7 ± 0.06 and was lower for G. monostachia in higher light. The pH of the solution in artificial tanks, unlike in plants, did not decrease over time. Aquaporin transcription was higher for plants with lower pH, but leaf hydraulic conductance did not differ, suggesting that the pH did not influence water uptake. Tank pH and CO 2 concentration were inversely related. Fusicoccin enhanced a decrease in tank pH, whereas orthovanadate did not., Conclusions: Guzmania monostachia acidified its tank water via leaf proton pumps, which appeared responsive to light. Low pH increased aquaporin transcripts but did not influence leaf hydraulic conductance, hence may be more relevant to nutrient uptake., (© 2022 The Authors. American Journal of Botany published by Wiley Periodicals LLC on behalf of Botanical Society of America.)

Magnetic Resonance (MR) imaging plays an important role in medical diagnosis and biomedical research. Due to the high in-slice resolution and low through-slice resolution nature of MR imaging, the usefulness of the reconstruction highly depends on the positioning of the slice group. Traditional clinical workflow relies on time-consuming manual adjustment that cannot be easily reproduced. Automation of this task can therefore bring important benefits in terms of accuracy, speed and reproducibility. Current auto-slice-positioning methods rely on automatically detected landmarks to derive the positioning, and previous studies suggest that a large, redundant set of landmarks are required to achieve robust results. However, a costly data curation procedure is needed to generate training labels for those landmarks, and the results can still be highly sensitive to landmark detection errors. More importantly, a set of anatomical landmark locations are not naturally produced during the standard clinical workflow, which makes online learning impossible. To address these limitations, we propose a novel framework for auto-slice-positioning that focuses on localizing the canonical planes within a 3D volume. The proposed framework consists of two major steps. A multi-resolution region proposal network is first used to extract a volume-of-interest, after which a V-net-like segmentation network is applied to segment the orientation planes. Importantly, our algorithm also includes a Performance Measurement Index as an indication of the algorithm's confidence. We evaluate the proposed framework on both knee and shoulder MR scans. Our method outperforms state-of-the-art automatic positioning algorithms in terms of accuracy and robustness., Competing Interests: Declaration of Competing Interest The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Yu Zhao, Ke Zeng, Yiyuan Zhao, Parmeet Bhatia, Mahesh Ranganath, Muhammed Labeeb Kozhikkavil, and Gerardo Hermosillo affiliated with Siemens Healthineers and Chen Li associate from Dartmouth College., (Copyright © 2022. Published by Elsevier B.V.)