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Deep Learning-Aided Lung Cancer Detection and

Lung cancer remains one of the most lethal types of cancer globally, emphasizing the necessity for early and accurate detection. This study suggests a deep learning-based classification model that uses the pretrained Xception model to distinguish between four different groups: normal lung tissue, squamous cell carcinoma, large cell carcinoma, and adenocarcinoma. We apply chest CT scan images of a chosen dataset meticulously to fine-tune the model utilizing image data improved by real-time preprocessing techniques. The model has a high accuracy rate, signifying its potential use in supporting clinical diagnosis. Grad-CAM (Gradient-weighted Class Activation Mapping) is used to map class-specific activation zones in order to improve explainability, which supports radiological diagnosis and makes explainable prediction poss ible. Our approach provides a promising AI-based solution to screen lung cancer with high accuracy and clinical interpretability.

Download PDF: https://toro.eu.org/RFqswI

Implementation of Obstetric Nursing Residency Programs in Brazil Using the Consolidated Framework for Implementation Research

Implementing evidence-based training for nurses is crucial for improving maternal health, especially in low- and middleincome countries. In Brazil, the Obstetric Nursing Residency Program of Pernambuco (ONRPP) was established to pro mote humanized childbirth care by developing the nursing workforce. This study used the Consolidated Framework for Implementation Research (CFIR) to analyze key factors affecting the program’s implementation. We conducted a qualita tive, multiple-case study at two reference hospitals in Pernambuco. Data were collected through document analysis, semistructured interviews with managers and preceptors, and a focus group with residents. We performed a directed content analysis based on the five domains and 39 constructs of the CFIR. D ata were coded and synthesized using MAXQDA to assess the valence and strength of each construct. Our findings revealed several facilitators, including the strong scientific basis of the training, high resident engagement, and a flexible program structure. However, we also identified significant barriers: lack of infrastructure and funding, weak inter-institutional communication, resistance from some clinical staff, and limited opportunities for feedback. Although residents reported increased autonomy and alignment with evidencebased practices, their role remained ambiguous for some staff. The ONRPP is a promising model for strengthening obstetric nursing and humanized care in Brazil. However, its long-term success requires strategic investment in leadership, communication, and organizational learning. This study contributes to the use of implementation frameworks like CFIR in nursing education and highlights their importance for guiding future scale-up strategies.

Downloa d PDF: https://soalf.eu.org/Oiowqt

C URRENT O PINION Spontaneous breathing trials: how and for how long?

(Abstract not found)

Download PDF: https://jawap.eu.org/LoLHa0

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(Abstract not found)

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Download PDF: https://csurat.eu.org/WjFD4v

Curing characteristics of flowable and sculptable bulk-fill composites

Objectives The aim of this study was to determine and correlate the degree of conversion (DC) with Vickers hardness (VH) and translucency parameter (TP) with the depth of cure (DoC) of five bulk-fill composites. Materials and methods Six specimens per group, consisting of Tetric EvoCeram Bulk Fill (BTEC Bulk,^ Ivoclar Vivadent), SonicFill (Kerr), SDR Smart Dentin Replacement (BSDR,^ Dentsply), Xenius base (BXenius,^ StickTech; commercialized as EverX Posterior, GC), Filtek Bulk Fill flowable (BFiltek Bulk,^ 3M ESPE), and Tetric EvoCeram (BTEC,^ control), were prepared for DC and VH: two 2-mm-thick layers, each light-cured for 10 s; one 4-mm bulk-fill, lightcured for 10 or 20 s; and one 6-mm bulk-fill, cured for 20 s. DC was measured using a Fourier-transform infrared spectrometer, VH using a Vi ckers hardness tester. DoC and TP were measured using an acetone-shaking test and a spectrophotometer, respectively. Data were analyzed using ANOVA and Pearson’s correlation (α = 0.05). Results DC and VH ranged between 40–70 % and 30– 80 VHN, respectively. TEC Bulk, Xenius, and SonicFill, bulk-filled as 4-mm-thick specimens, showed bottom-to-top hardness ratios above 80 % after 20 s curing. A positive linear correlation was found for bottom DC and VH. An average DC ratio of 0.9 corresponded to a bottom-to-top VH ratio of 0.8. Conclusions Sculptable bulk-fills require 20 s, whereas 10 s curing time was sufficient for flowable bulk-fills using a highintensity LED unit. Clinical relevance Clinicians should be aware that longer curing times may be required for sculptable than flowable bulkfill composites in order to achieve optimal curing characteristics.

Download PDF: https://soalc.eu.org/zDLue4