Multiple myeloma (MM) is a form of clonal plasma cell malignancy that associates with clinical manifestations such as anemia, hypercalcemia, bone pain, and renal impairment. Approximately 20-50% of MM patients at initial diagnosis experience renal injury, a vital complication that significantly influences prognosis and quality of life. This review seeks to clarify the multifaceted mechanisms of renal injury in MM, scrutinizing the pathogenic role of monoclonal proteins, the impact of hypercalcemia, and direct renal infiltration by plasma cells. Furthermore, it evaluates current diagnostic approaches, reviews management strategies, and highlights potential avenues for future research. By incorporating the latest scientific evidence and insights, this article aims to provide a comprehensive understanding of MM-associated renal impairment, offering a valuable resource for researchers and clinicians in handling this complex complication.

Pulmonary hypertension (PH), a syndrome characterized by elevated pulmonary pressures, commonly complicates connective tissue disease (CTD) and is associated with increased morbidity and mortality. The incidence of PH varies widely between CTDs; patients with systemic sclerosis are most likely to develop PH. Several different types of PH can present in CTD, including PH related to left heart disease and respiratory disease. Importantly, CTD patients are at risk for developing pulmonary arterial hypertension, a rare form of PH that is associated with high morbidity and mortality. Future therapies targeting pulmonary vascular remodeling may improve outcomes for patients with this devastating disease.

Concepts of multiple sclerosis (MS) biology continue to evolve, with observations such as \"progression independent of disease activity\" challenging traditional phenotypic categorization. Iron-sensitive, susceptibility-based imaging techniques are emerging as highly translatable MR imaging sequences that allow for visualization of at least 2 clinically useful biomarkers: the central vein sign and the paramagnetic rim lesion (PRL). Both biomarkers demonstrate high specificity in the discrimination of MS from other mimics and can be seen at 1.5 T and 3 T field strengths. Additionally, PRLs represent a subset of chronic active lesions engaged in \"smoldering\" compartmentalized inflammation behind an intact blood-brain barrier.

Tuberculosis (TB) is an infectious disease that remains one of the major global public health concerns. Early detection of Active Pulmonary TB is therefore of utmost importance for controlling lethality and disease spreading. Currently available TB diagnostics can be broadly categorized into microscopy, culture-based, and molecular approaches, all of which come with compromised sensitivity, limited efficacy, and high expenses. Hence, rapid, sensitive, and affordable diagnostic methods for TB is the current prerequisite for disease management. This review summarizes the proteomics investigations for host-specific biomarkers from serum, sputum, saliva, and urine samples of TB patients, along with patients having comorbidity. Thorough data mining from available literature led us to conclude that the host-specific proteins involved in immunity and defense, metabolic regulation, cellular adhesion, and motility, inflammatory responses, and tissue remodelling have shown significant deregulation upon Mycobacterium tuberculosis (Mtb) infection. Notably, the immunoregulatory protein orosomucoid (ORM) was up-regulated in active TB compared to non-TB individuals, as observed in multiple studies from diverse sample types. Mannose receptor C type 2 (MRC2) was identified as an upregulated, treatment response biomarker in two independent serum proteomics investigations. Thorough mechanistic investigation on these candidate proteins would be fascinating to dig into potential drug targets and customized therapeutics for TB patients, along with their diagnostic potentials.

Aims/Background Coronary heart disease is a common disease in the elderly and has a complex pathogenesis, which complicates the clinical diagnostic process. Thus, enhancing the diagnostic efficiency for coronary heart disease is imperative to improve the life expectancy of the elderly. This study aimed to explore the diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease. Methods The medical records of 421 patients with suspected coronary heart disease obtained from the geriatric department of the First Affiliated Hospital of Hebei North University from February 2020 to February 2023 were retrospectively analysed. After excluding 10 patients who did not meet the inclusion criteria, the remaining 411 patients were included in this study. The included subjects had undergone coronary computed tomography angiography and were divided into coronary heart disease group (n=208) and non-coronary heart disease group (n=203) according to the diagnostic results. Multimodal cardiovascular imaging (coronary computed tomography angiography and echocardiography) and detection of serum biomarkers such as small dense low-density lipoprotein, lipoprotein a, and gamma-glutamyl transferase were performed in both groups. The clinical indicators of the two groups were compared, and the combined diagnostic efficacy of multimodal cardiovascular imaging and biomarker detection was evaluated. Results Compared to the non-coronary heart disease group, the coronary heart disease group had significantly higher levels of maximum area stenosis, total plaque volume, total plaque burden and fibrotic plaque volume (p < ..001), and lower left ventricular ejection fraction level (p < ..001). Additionally, the coronary heart disease group exhibited higher levels of left ventricular end-diastolic volume, left ventricular end-systolic volume and stroke volume than the non-coronary heart disease group (p < ..001), and had higher levels of small dense low-density lipoprotein, lipoprotein a and gamma-glutamyl transferase (p < ..001). Our results demonstrated that combined diagnosis had better diagnostic efficacy than individual approaches, marked by higher area under the curve and sensitivity of the former (p < ..001). Conclusion Multimodal cardiovascular imaging technology combined with biomarker detection can distinctly improve the accuracy of coronary heart disease diagnosis in elderly patients.

OBJECTIVE: Single-lead electrocardiograms (ECGs) can be recorded using widely available devices such as smartwatches and handheld ECG recorders. Such devices have been approved for atrial fibrillation (AF) detection. However, little evidence exists on the reliability of single-lead ECG interpretation. We aimed to assess the level of agreement on detection of AF by independent cardiologists interpreting single lead ECGs, and to identify factors influencing agreement.
METHODS: In a population-based AF screening study, adults aged ≥65 years old recorded four single-lead ECGs per day for 1-4 weeks using a handheld ECG recorder. ECGs showing signs of possible AF were identified by a nurse, aided by an automated algorithm. These were reviewed by two independent cardiologists who assigned participant- and ECG-level diagnoses. Inter-rater reliability of AF diagnosis was calculated using linear weighted Cohen\'s kappa (kw).
RESULTS: Out of 2,141 participants and 162,515 ECGs, only 1,843 ECGs from 185 participants were reviewed by both cardiologists. Agreement was moderate: kw = 0.48 (95% CI, 0.37-0.58) at participant-level; and kw = 0.58 (0.53-0.62) at ECG-level. At participant-level, agreement was associated with the number of adequate-quality ECGs recorded, with higher agreement in participants who recorded at least 67 adequate-quality ECGs. At ECG-level, agreement was associated with ECG quality and whether ECGs exhibited algorithm-identified possible AF.
CONCLUSIONS: Inter-rater reliability of AF diagnosis from single-lead ECGs was found to be moderate in older adults. Strategies to improve reliability might include participant and cardiologist training and designing AF detection programmes to obtain sufficient ECGs for reliable diagnoses.

OBJECTIVE: To assess whether clinical and imaging characteristics predict hormonal subtype, growth, and adrenalectomy for incidental adrenal cortical adenomas (ACA).
METHODS: Single center cohort study.
METHODS: Consecutive adult patients with incidental ACA diagnosed between 2000 and 2016.
RESULTS: Of 1516 patients with incidental ACA (median age 59 years, 62% women), 699 (46%) had nonfunctioning adenomas (NFA), 482 (31%) had mild autonomous cortisol secretion (MACS), 62 (4%) had primary aldosteronism (PA), 39 (3%) had Cushing syndrome (CS), 18 (1%) had PA and MACS (PA-MACS), and 226 (15%) had incomplete workup. Age, sex, tumor size, and tumor laterality, but not unenhanced computed tomography Hounsfield units (HU), were associated with hormonal subtypes. In a multivariable analysis, ≥1cm growth was associated with younger age (odds ratio per 5-year increase, OR=0.8, P=0.0047) and longer imaging follow-up (OR=1.2 per year, P<.0001). Adrenalectomy was performed in 355 (23%) patients, including 38% of MACS and 15% of NFA. Adrenalectomy for NFA and MACS was more common in younger patients (OR=0.79 per 5-year increase, P=0.002), larger initial tumor size (OR=2.3 per 1-cm increase, P<.0001), ≥1cm growth (OR=15.3, P<.0001), and higher post-dexamethasone cortisol (OR=6.6 for >5 vs <1.8 mcg/dL, P=0.002).
CONCLUSIONS: Age, sex, tumor size, and laterality were associated with ACA hormonal subtype and can guide diagnosis and management. Tumor growth was more common with younger age and longer follow-up. Unenhanced HU did not predict hormonal subtype or growth. Adrenalectomy for MACS and NFA was mainly performed in younger patients with larger tumor size, growth, and elevated post-dexamethasone cortisol.

The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals.

BACKGROUND: This study investigated the potential of phosphorylated plasma Tau217 ratio (pTau217R) and plasma amyloid beta (Aβ) 42/Aβ40 in predicting brain amyloid levels measured by positron emission tomography (PET) Centiloid (CL) for Alzheimer\'s disease (AD) staging and screening.
METHODS: Quantification of plasma pTau217R and Aβ42/Aβ40 employed immunoprecipitation-mass spectrometry. CL prediction models were developed on a cohort of 904 cognitively unimpaired, preclinical and early AD subjects and validated on two independent cohorts.
RESULTS: Models integrating pTau217R outperformed Aβ42/Aβ40 alone, predicting amyloid levels up to 89.1 CL. High area under the receiver operating characteristic curve (AUROC) values (89.3% to 94.7%) were observed across a broad CL range (15 to 90). Utilizing pTau217R-based models for low amyloid levels reduced PET scans by 70.5% to 78.6%.
CONCLUSIONS: pTau217R effectively predicts brain amyloid levels, surpassing cerebrospinal fluid Aβ42/Aβ40\'s range. Combining it with plasma Aβ42/Aβ40 enhances sensitivity for low amyloid detection, reducing unnecessary PET scans and expanding clinical utility.
CONCLUSIONS: Phosphorylated plasma Tau217 ratio (pTau217R) effectively predicts amyloid-PET Centiloid (CL) across a broad spectrum. Integrating pTau217R with Aβ42/Aβ40 extends the CL prediction upper limit to 89.1 CL. Combined model predicts amyloid status with high accuracy, especially in cognitively unimpaired individuals. This model identifies subjects above or below various CL thresholds with high accuracy. pTau217R-based models significantly reduce PET scans by up to 78.6% for screening out individuals with no/low amyloid.

Covered anaerobic lagoons (CALs) are Latin America\'s main livestock waste treatment systems. Mexico has 680 CALs that present low biogas yields (0.05 m3 m-3 digester d-1) and low COD removal rates (< 60%). This work focused on diagnosing CAL´s low performance in dairy farms by determining and analyzing operational parameters. Seven CALs located in the main dairy basin of Mexico were analyzed. The sampling areas for each CAL were the supernatant, the active zone, settled sludge, and digester inlet and outlet. The variation of the process parameter values corroborated that CALs appeared stratified and not working as expected. The sludge zone, comprising 50-58% of total solids content and 1-15% of total CALs volume, showed an elemental compounds content suitable for organic fertilizer (340, 48, and 5 kg t-1 of C, N, and S, respectively). However, this zone contained, at least, 85% of the slowly hydrolysable material; the methanogenic potential was less than 87 mL CH4 g VS-1, and the C/N ratio ranged from 4.9 to 17, outside of the optimal range. The biogas produced did not exceed 60% of methane content and more than 3000 ppm of H2S. The sludge zone significantly influences the lagoon\'s dynamics since it is a nutrient sink. Furthermore, the lack of agitation is the leading cause for the low energy yield and the low removal of organic matter rate. This work provides valuable information to address the operational problems within the CALs improving our understanding that shall allow proposing reactivation alternatives.