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Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review
Background Breast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies. Methods A comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches. Results The search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR). Conclusion This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies.
Phenological strategies of an evergreen tree in the Caatinga
Seasonally Dry Tropical Forests (SDTF) account for 40% of global tropical forests, with the Caatinga standing out as the largest continuous formation of this type. However, the region faces severe threats, such as deforestation and desertification, which require urgent conservation efforts. In this context, understanding the adaptive strategies of native species becomes essential to support management actions. This study aims to identify the phenological strategies of Sarcomphalus joazeiro (Mart.), a species of high ecological, cultural, and economic importance in the region. Over two years, intrapopulation monitoring of vegetative and reproductive phenophases was conducted in a forest fragment in Rio Grande do Norte, Brazil, evaluating phenophase seasonality, reproductive synchrony, correlation with meteorological variables (precipitation, relative humidity, and air temperature), and fruit and seed biometrics. The results revealed that the flowering and fruiting of S. joazeiro are annual, synchronized, and occur during the dry season, highlighting an adaptive reproductive strategy and providing an important food source for the fauna. The species exhibited a weak correlation between its phenophases and meteorological variables, emphasizing its resistance to adverse climatic conditions. These characteristics make S. joazeiro unique among SDTF trees and underscore its ecological importance and potential for management and degraded area restoration strategies. Phenological studies with other Caatinga species are recommended to deepen understanding of biota-climate interactions and to contribute to effective conservation strategies.
Hypertension associated with serotonin reuptake inhibitors: A new analysis in the WHO pharmacovigilance database and examination of dose-dependency
Introduction Recent literature has reported instances of drug associated with hypertension with serotonin reuptake inhibitors (SRIs). Nonetheless, the association between SRIs and hypertension development is the subject of ongoing debate. It remains uncertain whether this is indicative of a class effect, and if dose-effect exist. To investigate the potential class effect associating SRIs with hypertension reporting, we utilized real-world data from VigiBase®, the World Health Organization (WHO) pharmacovigilance database. Methods We conducted an updated disproportionality analysis within VigiBase® to identify a signal of hypertension reporting with individual SRIs by calculating adjusted reporting odds ratios (aRORs) within a multivariate case/non-case study design. Additionally, we explored the presence of a dose-effect relationship. Results The database contained 13,682 reports of SRI associated with hypertension (2.2%), predominantly in women (70.0%). Hypertension was most reported in the 45-64 years old age group (44.8%). A total of 3,879 cases were associated with sertraline, 2,862 with fluoxetine, 2,516 with citalopram, 2,586 with escitalopram, 2,441 with paroxetine, 201 with fluvoxamine and 8 with zimeldine. A significant ROR was observed for all SRIs in both univariate (RORs ranging from 1.39 to 1.54) and multivariable analyses (aRORs ranging from 1.16 to 1.40) after adjustments for age group, sex, concurrent antihypertensive medication and drugs knowns to induce hypertension, except for fluvoxamine and zimeldine. No dose-response relationship was identified. Conclusion This investigation, conducted under real life conditions, unveils a notable pharmacovigilance safety signal associating SRI usage with hypertension reporting. No dose-response effect was detectable. Further longitudinal studies are warranted.
A new method for detection of microbursts via point observation methods and field measurement for validation study with Doppler weather radar
Wind shear (WS) phenomena are critical in many applications, especially in aviation, wind energy and urban planning. Microburst (MB) detection is important for ensuring safety during aircraft landing/takeoff, eliminating imbalances caused by shear from wind turbines, and for static calculations in urban planning. In this study, microburst events were detected using meteorological data. A new algorithm was applied to Light Detection and Ranging (LIDAR) data and 3 different cup anemometer data were available for 1-min and 10-min measurement periods. First, MB condition parameters using power law and basic wind shear analysis based on the scope of international criteria were defined, then checked in the algorithm. All results are compared with each other on behalf of detected microburst count, day, minute, and period. Detected events were matched at 66% and 85%, respectively, 10-min, and 1-min intervals. Validation studies were carried out for the same location by analysing the reflection values, reflection image and velocity product of the Doppler Weather Radar (DWR) with classical methods. However, when the radar results compared with 1- and 10-minute data sets, it was shown that 80% and 75% of daily events matched. The algorithm provided good continuity across LIDAR, different cup anemometers, and the weather radar. Consequently, the new algorithm will provide a great economic advantage.
Negative impact of chemotherapy on kinetic growth rate of the future liver remnant if applied following PVE or ALPPS
Purpose Modern liver surgery has improved the percentage of potentially resectable malignant tumors. However, if the future liver remnant is small, patients remain at risk of developing postoperative liver failure. Thus, the future liver remnant must be increased, while at the same time, the primary tumor may have to be controlled by chemotherapy. To address this conflict, we retrospectively analyzed the changes in hypertrophy before and after Associating Liver Partition with Portal vein ligation for Staged hepatectomy (ALPPS) or Portal Vein Embolization (PVE), with or without parallel systemic chemotherapy. Materials and Methods We retrospectively analysed 172 patients (54 female and 118 male), treated with ALPPS in 90 patients (median age 61 years [Q1, Q3: 52,71]) and with PVE in 82 patients (median age 66 years [Q1, Q3: 56,73]). The median control interval was 4.9 [Q1, Q3: 4.0, 6.0] weeks after the PVE, and 2.6 [Q1, Q3: 1.6, 5.8] weeks after ALPPS step 1. Results The overall kinetic growth rate (median) for the entire group was 0.02 (2%) per week. When systemic chemotherapy was administered prior to intervention, the kinetic growth rate of these treated patients (vs. untreated) exhibited a median of 0.020 [Q1, Q3: 0.011, 0.067] compared to 0.024 [Q1, Q3: 0.013, 0.041] (p = 0.949). When chemotherapy was administered after the PVE/ ALPPS treatment, the kinetic growth rate declined from a median of 0.025 [Q1, Q3: 0.013, 0.053] to 0.011 [Q1, Q3: 0.007, 0.021] (p = 0.005). Subgroup analysis showed statistically significant effects only in the PVE group (median ALPPS -45% (p = 0.157), PVE -47% (p = 0.005)). Conclusion This retrospective analysis indicated that systemic chemotherapy given after PVE/ the first step of the ALPPS procedure, i.e., the growth phase, has a negative effect on the kinetic growth rate.
Expression of Concern: Assessing clinical and morphological features of megalotrichosis induced by Tyrosine kinase inhibitors versus Prostaglandins analogues
AI tools are spotting errors in research papers: inside a growing movement
Ice-hunting Moon lander runs into trouble ― leaving scientists in suspense
‘I was told to toughen up’: is academia getting resilience all wrong?
Daily briefing: This key protein could be responsible for brain ageing
Kleptomania on the impulsive–compulsive spectrum. Clinical and therapeutic considerations for women
A study of dynamic functional connectivity changes in flight trainees based on a triple network model
Impact of Amazonian deforestation on precipitation reverses between seasons
Rapid identification of Lonicera japonica via Proofman-LMTIA technology
Abstract The study introduces a novel, rapid and precise method for identifying Lonicera japonica (JYH), an important medicinal herb, utilizing Proofman-LMTIA (Proofman probe-ladder melting temperature isothermal amplification) technology. Based on the 5.8 S-ITS2 rDNA sequence, specific primers and Proofman probes were designed to distinguish JYH from similar species, addressing the frequent market adulteration. The method’s optimal reaction conditions were established at 67 °C, ensuring high specificity. Sensitivity test demonstrated this method could detect as little as 10 pg/µL of JYH genomic DNA, with a detection limit of 1% (v/v) in adulteration experiments. The Proofman-LMTIA method successfully authenticated all eight market slice samples of JYH, six drugs and one solid beverage, achieving a 100% positive detection rate. This technique represents a significant advancement in medicinal material quality control, ensuring the JYH authenticity. These findings underscore the potential of the Proofman-LMTIA as a reliable, cost-effective, and user-friendly tool for real-time quality assessment in the herbal medicine industry.
The association between abdominal obesity and pulmonary function trajectories among patients with COPD
A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform
Abstract This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8% on the Icentia11k dataset using four arrhythmia classes and 99.58% on the MIT-BIH dataset using five arrhythmia classes.
Real-time inference for binary neutron star mergers using machine learning
Abstract Mergers of binary neutron stars emit signals in both the gravitational-wave (GW) and electromagnetic spectra. Famously, the 2017 multi-messenger observation of GW170817 (refs. 1,2) led to scientific discoveries across cosmology3, nuclear physics4–6 and gravity7. Central to these results were the sky localization and distance obtained from the GW data, which, in the case of GW170817, helped to identify the associated electromagnetic transient, AT 2017gfo (ref. 8), 11 h after the GW signal. Fast analysis of GW data is critical for directing time-sensitive electromagnetic observations. However, owing to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here we present a machine-learning framework that performs complete binary neutron star inference in just 1 s without making any such approximations. Our approach enhances multi-messenger observations by providing: (1) accurate localization even before the merger; (2) improved localization precision by around 30% compared to approximate low-latency methods; and (3) detailed information on luminosity distance, inclination and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.