Rheumatic tricuspid valve disease: Repair versus Replacement

Background: Tricuspid valve disease is most commonly functional, however, organic affection still accounts for one fourth of cases. Rheumatic fever which is endemic in Egypt is a main cause of organic affection. Current practice largely relies on tricuspid valve repair; however, it has been difficult to determine optimal procedure. Objectives: Herein, we study the outcome of replacement versus repair in such patients. Patients and methods: A prospective study was conducted on 300 consecutive patients with rheumatic heart disease showing severe tricuspid valve affection underwent tricuspid valve surgery, between 2014 and 2018. The patients were divided into two groups; TVR group (n=150) which included patients who underwent tricuspid valve replacement and TVr group (n=150) which included patients who underwent tricuspid valve repair. Diagnosis and follow up were done by echocardiography. Peri-operative variables, clinical outcome, morbidity, mortality, and follow up data were recorded. Results: Mean follow-up was 4±1.32 years. In-hospital mortality was 6 patients (4%) in TVR group and 3 patients (2%) in TVr group (P value ≥ 0.05). Postoperative low cardiac output syndrome and stroke were significantly higher in the repair group. Postoperative RV dysfunction, renal impairment, renal failure and chest re-exploration were significantly higher in the replacement group. Severe tricuspid regurgitation was reported in 19 patients (12.6%) of the repair group on follow up. Conclusion: Tricuspid valve repair is preferable to replacement to avoid the drawbacks of prosthesis. However, tricuspid valve replacement is feasible with comparable survival outcome and the progressive nature of the rheumatic disease may recommend replacement

Semantic segmentation of microbial alterations based on SegFormer

IntroductionPrecise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions.MethodsThree distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation.ResultsThe results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general.DiscussionThe findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration

U(VI) and Th(IV) recovery using silica beads functionalized with urea- or thiourea-based polymers – Application to ore leachate

International audienceUrea and thiourea have been successfully deposited at the surface of silica beads (through one-pot reaction with formaldehyde) for the recovery of U(VI) and Th(IV) to produce UR/SiO2 and TUR/SiO2 composites, respectively. These materials have been characterized by FTIR, titration, elemental analysis, BET, TGA, SEM-EDX for identification of structural and chemical properties, and interpretation of binding mechanisms. Based on deprotonation of reactive groups (amine, carbonyl, or thiocarbonyl) and metal speciation, the optimum pH was ~4. Uptake kinetics was fast (equilibrium within 60–90 min). Although the kinetic profiles are fitted by the pseudo-first order rate equation, the resistance to intraparticle diffusion cannot be neglected. Sorption isotherms were fitted by Langmuir equation (maximum sorption capacities: 1–1.2 mmol g−1). Thermodynamics are also investigated showing differences between the two types of functionalized groups: exothermic for TUR/SiO2 and endothermic for UR/SiO2. Metal desorption is highly effective using 0.3–0.5 M HCl solutions: total desorption occurs within 30–60 min; sorption/desorption properties are remarkably stable for at least 5 cycles. The sorbents have marked preference for U(VI) and Th(IV) over alkali-earth and base metals at pHeq ~4.8. By preliminary precipitation steps it is possible “cleaning” ore leachates of pegmatite ore, and recovering U(VI) and Th(IV) using functionalized silica beads. After elution and selective recovery by precipitation with oxalate (Th-cake) and alkaline (U-cake), the metals can be valorized

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing