Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

RATIONALE AND OBJECTIVES: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. MATERIALS AND METHODS: A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS: Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. CONCLUSION: Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy.

Coffee is a daily essential, with prices varying based on taste, aroma, and chemical composition. However, distinguishing between different coffee beans is challenging due to time-consuming and destructive sample pretreatment. This study presents a novel approach for directly analyzing single coffee beans through mass spectrometry (MS) without the need for sample pretreatment. Using a single coffee bean deposited with a solvent droplet containing methanol and deionized water, we generated electrospray to extract the main species for MS analysis. Mass spectra of single coffee beans were obtained in just a few seconds. To showcase the effectiveness of the developed method, we used palm civet coffee beans (kopi luwak), one of the most expensive coffee types, as model samples. Our approach distinguished palm civet coffee beans from regular ones with high accuracy, sensitivity, and selectivity. Moreover, we employed a machine learning strategy to rapidly classify coffee beans based on their mass spectra, achieving 99.58% accuracy, 98.75% sensitivity, and 100% selectivity in cross-validation. Our study highlights the potential of combining the single-bean MS method with machine learning for the rapid and non-destructive classification of coffee beans. This approach can help to detect low-priced coffee beans mixed with high-priced ones, benefiting both consumers and the coffee industry.

Nomogram for predicting skip metastasis in cN0 papillary thyroid cancer patients at increased risk of lymph node metastasis.

BACKGROUND: Skip lymph node metastasis (SLNM) refers to lateral lymph node metastasis (LLNM) without involving central lymph node (CLN). Some microscopic nodal positivity may be difficult to detect before surgery due to atypical imaging characteristics. These patients are misdiagnosed as having clinically node-negative (cN0) papillary thyroid cancer (PTC) even after central lymph node dissection, leading to a high risk of developing LNM after surgery. Current prediction models have limited clinical utility, as they are only applicable to predict SLNM from clinically node-positive (cN+) PTC, not cN0 PTC, and this has little impact on treatment strategies. OBJECTIVES: This study aimed to establish a nomogram for preoperatively assessing the likelihood of SLNM in cN0 PTC patients with increased risk of LNM, thus optimizing their therapeutic options. MATERIAL AND METHODS: The records of 780 PTC patients undergoing thyroidectomy along with bilateral central lymph node dissection were retrospectively reviewed. The cN0 patients with postoperative LLNM (occult SLNM) and cN+ patients without central lymph node metastasis (CLNM) (typical SLNM) were included in the SLNM group (n = 82). The CLNM-negative cN0 patients without postoperative LLNM were assigned to the non-SLNM group (n = 698). The independent correlates of SLNM constituted the nomogram for determining the likelihood of SLNM in high-risk cN0 PTC patients. RESULTS: The independent correlates of SLNM were age (hazard ratio (HR) = 1.016), tumor location (HR = 1.801), tumor size (HR = 1.528), and capsular invasion (HR = 2.941). They served as components in the development of the nomogram. This model was verified to present acceptable discrimination. It showed good calibration and a decent net benefit when the predicted probability was <60%. CONCLUSIONS: We developed a nomogram incorporating preoperative clinical data to predict the probability of SLNM development in high-risk cN0 PTC patients, which contributed to their optimized treatment options.

Sleep patterns and potential risk factors for disturbed sleep quality in patients after surgery for infective endocarditis.

BACKGROUND: The current study aimed to investigate the sleep quality of patients after valve replacement surgery due to infective endocarditis and identify risk factors for disturbed sleep post hospitalisation. METHODS: Eighty patients were assessed postoperatively using subjective scale measures, the Pittsburgh sleep quality index (PSQI) and the Epworth sleepiness scale, and an objective measure, actigraphy. Scale measures were assessed approximately 2 weeks and 6 months after surgery. Actigraphy monitoring was performed for 2 consecutive weeks during hospitalisation. Logistic regression was used to identify risk factors for disturbed sleep. RESULTS: The study population (n = 80) had an average age of 42.8 ± 14.2 years, and 67.5% were male. The median sleep efficiency was 85.3% in week 1 and 86.8% in week 2. The frequency of awakenings was significantly higher in week 1 (20.0 times vs. 19.3 times, p = 0.017). The scale measures showed significant improvement in sleep by 6 months after surgery compared to that during hospitalisation. Multivariable logistic regression analysis suggested that the possible risk factors for disturbed sleep 6 months after surgery included age (OR = 1.479, 95%CI 1.140-1.920) and a few parameters of early postoperative disturbed sleep quality (PSQI: OR = 2.921, 95%CI 1.431-5.963; sleep efficiency: OR = 0.402, 95%CI 0.206-0.783; and average duration of awakenings: OR = 0.006, 95%CI 0.000-0.827). CONCLUSIONS: Disturbed sleep quality was witnessed in postoperative patients during hospitalisation and up to 6 months after surgery. Over time, the patients' sleep quality improved significantly. Age and a few early postoperative sleep quality variables were risk factors for disturbed sleep 6 months after surgery.

Direct Mass Spectrometric Analysis of Semivolatiles Derived from Real Samples at Atmospheric Pressure.

This study demonstrated a facile ionization method with the use of real samples for the ionization of their main compositions at ambient conditions for mass spectrometric analysis. Analyte ions derived from the real samples were readily observed in the mass spectrum when placing the samples close (≤1 mm) to the inlet of the mass spectrometer applied with a high voltage. No additional accessories such as an ionization emitter, a plasma generator, or a high voltage power supply were required for this approach. Ionization of semivolatiles derived from the samples occurred between the samples and the inlet of the mass spectrometer presumably owing to the dielectric breakdown induced by the electric field provided by the mass spectrometer. Real samples including plants, medicine tablets, and gloves with contaminants were used as the model samples. The putative ionization mechanisms are also discussed in this study.

Ionization of Volatile Organics and Nonvolatile Biomolecules Directly from a Titanium Slab for Mass Spectrometric Analysis.

Atmospheric pressure chemical ionization (APCI)-mass spectrometry (MS) and electrospray ionization (ESI)-MS can cover the analysis of analytes from low to high polarities. Thus, an ion source that possesses these two ionization functions is useful. Atmospheric surface-assisted ionization (ASAI), which can be used to ionize polar and nonpolar analytes in vapor, liquid, and solid forms, was demonstrated in this study. The ionization of analytes through APCI or ESI was induced from the surface of a metal substrate such as a titanium slab. ASAI is a contactless approach operated at atmospheric pressure. No electric contacts nor any voltages were required to be applied on the metal substrate during ionization. When placing samples with high vapor pressure in condensed phase underneath a titanium slab close to the inlet of the mass spectrometer, analytes can be readily ionized and detected by the mass spectrometer. Furthermore, a sample droplet (~2 µL) containing high-polarity analytes, including polar organics and biomolecules, was ionized using the titanium slab. One titanium slab is sufficient to induce the ionization of analytes occurring in front of a mass spectrometer applied with a high voltage. Moreover, this ionization method can be used to detect high volatile or polar analytes through APCI-like or ESI-like processes, respectively.

The Assessment of Sleep Quality in Patients Following Valve Repair and Valve Replacement for Infective Endocarditis: A Retrospective Study at a Single Center.

BACKGROUND The aim of this study was to measure sleep quality among patients who underwent infective endocarditis (IE) surgery and identify the risk factors involved in sleep disorders. MATERIAL AND METHODS In this study, we used actigraphy, the Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleep Scale (ESS) to determine the clinical characteristics of sleep disorders in 116 patients with IE who were in rehabilitation after surgery. RESULTS Our results showed that 46 (39.7%) patients had sleep efficiency over 85%, while 70 (60.3%) patients had sleep efficiency below 85%. The correlation analysis showed that sleep efficiency was related to the duration of the disease, with a longer duration leading to lower sleep efficiency (P=0.031). The sleep efficiency of patients with IE following surgery was also affected by alcohol consumption; however, surprisingly, patients with "heavy" alcohol consumption had higher sleep efficiency (P=0.030). We found a significant correlation between sleep efficiency and postoperative interleukin-6 (IL) levels, C-reactive protein (CRP) levels, and preoperative erythrocyte sedimentation rate (P<0.05). No significant correlation was found between brain natriuretic peptide levels and sleep efficiency, PSQI score, or ESS score. Postoperative hemoglobin (Hb) level was associated with sleep efficiency (R=0.194, P=0.036), but there was no statistically significant correlation between the PSQI and ESS scores. Postoperative alanine transaminase (ALT) showed a significant negative correlation with sleep efficiency (R=-0.27, P=0.003). CONCLUSIONS We found a high prevalence of sleep disorders in patients with IE along with an increase in inflammatory factors, including postoperative IL-6, CRP, ALT, and Hb levels.

Analysis of sleep characteristics and clinical outcomes of 139 adult patients with infective endocarditis after surgery.

BACKGROUND: Little is known about the postoperative sleep quality of infective endocarditis patients during hospitalization and after discharge. AIM: To investigate the sleep characteristics of infective endocarditis patients and to identify potential risk factors for disturbed sleep quality after surgery. METHODS: The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale were used to assess patient sleep quality. Logistic regression was used to explore the potential risk factors. RESULTS: The study population (n = 139) had an average age of 43.40 ± 14.56 years, and 67.6% were men (n = 94). Disturbed sleep quality was observed in 86 patients (61.9%) during hospitalization and remained in 46 patients (33.1%) at 6 mo after surgery. However, both PSQI and Epworth Sleepiness Scale scores showed significant improvements at 6 mo (P < 0.001 and P = 0.001, respectively). Multivariable logistic regression analysis showed that the potential risk factors were age (odds ratio = 1.125, 95% confidence interval: 1.068-1.186) and PSQI assessed during hospitalization (odds ratio = 1.759, 95% confidence interval: 1.436-2.155). The same analysis in patients with PSQI ≥ 8 during hospitalization suggested that not using sleep medication (odds ratio = 15.893, 95% confidence interval: 2.385-105.889) may be another risk factor. CONCLUSION: The incidence of disturbed sleep after infective endocarditis surgery is high. However, the situation improves significantly over time. Age and early postoperative high PSQI score are risk factors for disturbed sleep quality at 6 mo after surgery.

Using gadolinium ions as affinity probes to selectively enrich and magnetically isolate bacteria from complex samples.

Magnetic trapping has been employed in the development of analytical methods owing to its ease and simplicity in handling samples. Nevertheless, the generation of functional probes is usually time consuming. A new and simple affinity method that uses gadolinium ion (Gd3+), a magnetic ion, as affinity probe for magnetic tapping of pathogenic bacteria was demonstrated in the present study. Escherichia coli O157:H7, Staphylococcus aureus, and Acinetobacter baumannii were selected as model bacteria. The model bacteria were magnetically isolated after incubation in Tris buffer (pH 8) containing Gd3+ (0.1 M) under microwave heating (power: 180 W, 90 s × 3). The resultant Gd3+-bacterium conjugates possessed sufficient magnetism, resulting in magnetic aggregations by an external magnet (∼4,000 Gauss). For ease of magnetic isolation, the sample containing Gd3+-bacterium complexes was stirred by a small magnet. After 1 h, the magnet attached with precipitates, i.e., Gd3+-bacterium conjugates, was readily removed using a pair of tweezers. The bacteria in the resultant conjugates were characterized by matrix-assisted laser desorption/ionization mass spectrometry. The limits of detection of the current approach toward E. coli O157:H7, S. aureus, and A. baumannii in complex samples were ∼104-105 cells mL-1.