Severe Overlap Research Articles

Spectrofluorometric Turn-Off Sensing Probe for Tolmetin Determination Using Cerium and Nitrogen Codoped Carbon Dots.

A facile, low-cost, and rapid method was developed for spectrofluorometric determination of tolmetin (TOL) using highly fluorescent (QY = 35%) cerium and nitrogen codoped carbon dots (Ce-NCDs) sensing platform prepared by one-step hydrothermal method. Owing to the severe overlap between TOL absorption and Ce-NCDs excitation spectra around 320 nm, TOL could attenuate the emission of Ce-NCDs at 407 nm through primary inner filter effect (IFE). This concentration dependent attenuation was linear between 2 and 10 μg/mL (r = 0.9993) with a limit of detection of 0.5 μg/mL. The sensor showed very good accuracy (% recovery = 98%-100%) and precision (%RSD < 2). The developed sensing strategy was successfully applied for TOL detection as an active pharmaceutical ingredient in pharmaceutical formulation and as a potential pollutant in environmental water samples, such as tap and river water, with acceptable recovery and considerable selectivity. Thus, the direct single-step spectrofluorometric determination of TOL was enabled without extensive processing or chemical derivatization.

Prospective evaluation of transcatheter aortic valve implantation (TAVI) with coronary alignment and impact on coronary access

Abstract Introduction Transcatheter aortic valve implantation (TAVI) with optimised coronary alignment aims to reduce coronary-commissural overlap (CCO) to facilitate future coronary intervention and reduce the risk of coronary obstruction after re-do TAVI, however, in-vivo data are lacking. Aims To determine the feasibility of TAVI with coronary alignment assessed by post-TAVI CT. Secondary endpoints included the incidence of adverse anatomical features which could impede coronary access (severe CCO and ostia below valve skirt) and their association with coronary engagement during invasive angiography. Methods 30 patients underwent TAVI with optimised coronary alignment with either ACURATE neo2 or Evolut Pro+ and invasive coronary angiography and cardiac CT performed after valve deployment. Results 15 patients received ACURATE neo2 (50%) and 26 patients (86.7%) underwent CT after TAVI. The mean inter-coronary angle was 140 (19.5)° and the median observed angular deviation from perfect alignment (0°) was 0.8° (IQR -4.5 to +10) ° with perfect alignment (±15°) observed in 21/26 cases (80.8%) and ≤mild misalignment (±30°) in 24/26 of cases (92.3%). The remaining two cases had moderate misalignment (30-45°) with no cases of severe misalignment. The angle between the coronary ostia and the nearest TAVI commissure was 46 (40 - 57) ° for the RCA and 50.5 (43 - 54) ° for the LCA which did not differ significantly from the native situation (both p&amp;gt;0.2), with 1 (3.8%) case of severe overlap. 46/55 (83.6%) coronary engagements were selective, 7/55 (12.7%) semi-selective and 2/55 (3.6%) unable to intubate. Non-selective engagement/unable to engage affected the RCA more than the LCA (6/27 [22.2%] vs. 3/28 [10.7%]) and was three-fold more common with the Evolut (7/29 [24.1%]) than the Acurate (2/26 [7.7%]. Coronary&amp;gt;commissural angle in those with unselective engagement was 43° (IQR 37 to 51°). Conclusions TAVI with optimised coronary alignment is technically feasible, safe and reduces coronary-commissural overlap which is important for future re-do TAVI procedures. Difficult coronary access was encountered more frequently following Evolut Pro+ and when engaging the RCA, but was unrelated to angular distance to nearest commissure.

Disentangling the Complexity in Protein Complexes Using Complementary Isotope-Labeling and Multiple-Receiver NMR Spectroscopy.

Intrinsically disordered proteins are abundant in eukaryotic systems, but they remain largely elusive pharmacological targets. NMR spectroscopy proved to be a suitable method to study these proteins and their interaction with one another or with drug candidates. Although NMR can give atomistic information about these interplays, molecular complexity due to severe spectral overlap, limited sample stability, and quantity remain an issue and hamper widespread applications. Here, we propose an approach to simultaneously map protein-protein binding sites onto two interacting partners by employing a complementary isotope-labeling strategy and a multiple receiver NMR detection scheme. With one partner being 15N,2H labeled and the interacting one being 13C,1H-labeled, we exploited proton and carbon detection to obtain clean and easily readable information. The method is illustrated with an application to the 50 kDa ternary protein complex formed between the prominent oncogenic transcription factor complex Myc/MAX and the tumor suppressor BRCA1.

A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting.

In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit's effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.

Extracting Scalar Couplings From Complex 1H NMR Spectra Using a Simple 2D J-Resolved Sequence.

Measurement of scalar couplings between protons is a very challenging task because of complex multiplet patterns and severe overlapping of these multiplets in congested 1D spectra. Numerous 2D J-resolved sequences now exist that utilize either the Zangger-Sterk or PSYCHE or z-filter elements along with selective refocusing and pure-shift schemes to generate high-resolution phase-sensitive spectra with simple doublets in dimension. Herein, we present a 2D J-resolved sequence that employs a simple element consisting of hard pulses and inter-pulse delays to generate phase-sensitive spectra. This simple element in combination with selective refocusing eliminates all the undesired components including the intense axial peaks, thus provides clean 2D J-resolved spectra with signals of only two targeted protons with simple doublets in dimension and full multiplets of target protons in dimension. This high selectivity thus obviates the need for extra filtering elements and pure-shift acquisition schemes that are integrated into existing sequences to facilitate coupling measurements in overcrowded signals. It is therefore anticipated that this sequence, with the ease of implementation and ability to extract coupling values from highly congested spectra, should turn out an important tool for structural and conformational analyses in chemical and biological studies.

Coinfection of Klebsiella pneumoniae and Aspergillus in a patient with chronic obstructive pulmonary disease post cardiac arrest: a case report

IntroductionChronic obstructive pulmonary disease is a lung condition characterized by chronic respiratory symptoms (breathlessness, cough, and expectoration). In the advanced stages, patients often report to the Accident & Emergency department due to worsening of symptoms. Because of the repeated exposure to corticosteroids during the management of exacerbations, these patients are susceptible to super additional infections. Pulmonary aspergillosis can be divided into three main categories: invasive pulmonary aspergillosis, allergic bronchopulmonary aspergillosis and chronic pulmonary aspergillosis. Aspergillus overlap syndrome is defined as the presence of more than one form of Aspergillus in a single patient. However, coinfection with Klebsiella and pulmonary aspergillosis overlap syndrome is rare and poses a treatment challenge. As per a pub med search, no such case report has been reported in a case of chronic obstructive pulmonary disease.Case reportWe report the case of a 66-year-old male, Punjabi Hindu by ethnicity, who was a reformed smoker with a known case of COPD. He presented with a history of breathlessness (mMRC grade 4) associated with cough with expectoration and wheezing for 15 days and intermittent episodes of hemoptysis for more than 6 months. The examination revealed tachypnea and wheezing throughout the lung fields. He was initially managed with parenteral steroids and frequent nebulization with bronchodilators. On day 5 of hospitalization, the patient experienced worsening of symptoms and cardiac arrest; he was intubated and return of spontaneous circulation was achieved within 5 minutes of cardio pulmonary resuscitation. Tracheal aspirate and culture revealed Aspergillus fumigatus and Klebsiella pneumoniae respectively. He underwent chest CT, which showed features suggestive of allergic bronchopulmonary aspergillosis and invasive pulmonary aspergillosis. He was found to have elevated β-d-glucan, galactomannan, and aspergillus IgE and IgG. Severe pneumonia and pulmonary Aspergillus overlap syndrome were managed with antibiotics, steroids, and antifungals. Over the next 15–20 days, his general condition improved. He was discharged after 45 days of hospitalization and continued on oral corticosteroids, antifungals, and inhaled bronchodilators.ConclusionCoinfection with bacteria and fungi worsens the outcome. Clinicians should be aware of the polymicrobial manifestations and various drug interactions involved. Timely diagnosis aids in better management strategies and improved patient outcomes.

Ultraselective, Ultrahigh Resolution 1D TOCSY

AbstractSolution state 1H NMR spectroscopy provides valuable insights into molecular structure and conformation. However, when the spectrum exhibits severe signal overlap, it hampers the extraction of key structural information. Here, an ultraselective, ultrahigh resolution TOCSY method is introduced that greatly reduces spectral complexity, allowing the extraction of previously inaccessible spectral information. It combines the recently developed GEMSTONE excitation with homonuclear decoupling to provide highly simplified through‐bond correlation 1D 1H NMR spectra, showing all signals within the selected spin system as singlets. The new method can greatly facilitate the analysis of mixtures, as shown here for a mixture of Cinchona alkaloids (popular catalysts in asymmetric synthesis) and a mixture of glucocorticoids (used for treating conditions such as asthma).

InteractNet: Social Interaction Recognition for Semantic-rich Videos

The overwhelming surge of online video platforms has raised an urgent need for social interaction recognition techniques. Compared with simple short-term actions, long-term social interactions in semantic-rich videos could reflect more complicated semantics such as character relationships or emotions, which will better support various downstream applications, e.g., story summarization and fine-grained clip retrieval. However, considering the longer duration of social interactions with severe mutual overlap, involving multiple characters, dynamic scenes, and multi-modal cues, among other factors, traditional solutions for short-term action recognition may probably fail in this task. To address these challenges, in this article, we propose a hierarchical graph-based system, named InteractNet, to recognize social interactions in a multi-modal perspective. Specifically, our approach first generates a semantic graph for each sampled frame with integrating multi-modal cues and then learns the node representations as short-term interaction patterns via an adapted GCN module. Along this line, global interaction representations are accumulated through a sub-clip identification module, effectively filtering out irrelevant information and resolving temporal overlaps between interactions. In the end, the association among simultaneous interactions will be captured and modelled by constructing a global-level character-pair graph to predict the final social interactions. Comprehensive experiments on publicly available datasets demonstrate the effectiveness of our approach compared with state-of-the-art baseline methods.

Multiparameter gas sensor based on UV-SFR combined with wavelet transform-neural network at ppb level

Abnormal concentrations of nitric oxide (NO), ammonia (NH3), carbon disulfide (CS2) and sulphur dioxide (SO2) in exhaled breath are closely associated with diseases in specific organs of the body. Meanwhile, the above gases are also common pollutants in the atmosphere, and effective online detection of them plays a key role in disease prevention and environmental pollution prevention. This study reports a multiparameter gas sensor system based on the combination of ultraviolet segmented fitting reduction (UV-SFR) and wavelet transform-neural network (WT-NN) model for simultaneous online detection of the concentrations of NO, NH3, CS2 and SO2. Firstly, to avoid the occurrence of spectral baseline drift and absorption peak intensity reduction, a UV-SFR method is proposed for the discrete single peak type absorption spectra contained in the UV spectra. Our method can remove the slow change absorption induced by other factors, and ultimately obtain the differential absorption spectra of the gas mixtures. Then, the WT is used to denoise the differential absorption spectra of the gas mixtures. Next, for the presence of severe spectral line overlap in gas mixtures, the NN model is built based on the denoised spectral data. Finally, transfer learning is introduced into NN to improve the detection accuracy. The experimental results show that the sensor system can reliably achieve simultaneous online detection of NO, NH3, CS2, and SO2 at ppb level, and the average absolute errors of their measurements are 0.96 ppb, 1.27 ppb, 0.76 ppb and 0.97 ppb, with measurement accuracies of 3.5 %, 0.8 %, 0.5 % and 0.9 %, respectively.

A Gaussian-mixture-model based peak splitting method in X-ray fluorescence for high concentration ratio solution

Simultaneous detection of multi-heavy metal ions in zinc sulfate solution facilitates the zinc hydrometallurgy process control. X-ray fluorescence spectrometry is widely used for composition detection due to its high detection speed and no sample pre-treatment. However, the high matrix ion-to-trace impurity ion ratio in zinc sulfate causes severe spectral overlap, hindering pure peak area acquisition—a vital feature in ion concentration detection. To address this, a peak-splitting method based on the Gaussian mixture model was proposed. Initially, two ion mixing spectra were obtained with optimized reagents. Subsequently, Gaussian models were established for the characteristic peak regions with varying distributions. Finally, the proposed model was combined with Chaotic Sparrow Search Algorithm to handle the overlapping peaks. The experimental results demonstrated superior stability and additivity compared to other methods. All coefficients of determination between single peak areas and ion concentrations exceeded 0.99, confirming the method's efficacy in resolving heavily masked overlapping peaks.

Commissural vs Coronary Alignment to Avoid Coronary Overlap With THV-Commissure in TAVR: A CT-Simulation Study

BackgroundCoronary alignment is proposed as an alternative to commissural alignment for reducing coronary overlap during transcatheter aortic valve replacement (TAVR). However, largescale studies are lacking. ObjectivesThis study aimed to determine the incidence of coronary overlap with commissural vs coronary alignment using computed tomography (CT) simulation in patients undergoing TAVR evaluation. MethodsIn 1,851 CT scans of native aortic stenosis patients undergoing TAVR evaluation (April 2018 to December 2022),virtual valves simulating commissural and coronary alignment were superimposed on axial aortic root images. Coronary overlap was assessed based on the angular gap between coronary artery origin and the nearest transcatheter heart valve commissure, categorized as severe (≤15°), moderate (15°-30°), mild (30°-45°), and no-overlap (45°-60°). ResultsThe overall incidence of moderate/severe and severe overlap with either coronary artery remained rare with either coronary or commissural alignment (coronary 0.52% left, 0.52% right; commissural 0.30% left, 3.27% right). Comparing techniques, coronary alignment reduced moderate/severe overlap only for the right coronary artery (0.38% vs 2.97%; P <0.0001). For the left coronary artery, both techniques showed similar moderate/severe overlap, but commissural alignment had significantly higher no-overlap rates (91.1% vs 84.9%; P < 0.0001). Fluoroscopic angle during valve deployment was strongly correlated between commissural and coronary alignment (r = 0.80; P < 0.001). ConclusionsUsing CT simulation, the incidence of coronary overlap with transcatheter heart valve-commissure is rare with commissural alignment. Coronary alignment reduced right coronary overlap, whereas commissural alignment had higher rates of no left coronary overlap. Coronary alignment should be reserved only when commissural alignment results in severe coronary overlap.

AG-YOLO: A Rapid Citrus Fruit Detection Algorithm with Global Context Fusion

Citrus fruits hold pivotal positions within the agricultural sector. Accurate yield estimation for citrus fruits is crucial in orchard management, especially when facing challenges of fruit occlusion due to dense foliage or overlapping fruits. This study addresses the issues of low detection accuracy and the significant instances of missed detections in citrus fruit detection algorithms, particularly in scenarios of occlusion. It introduces AG-YOLO, an attention-based network designed to fuse contextual information. Leveraging NextViT as its primary architecture, AG-YOLO harnesses its ability to capture holistic contextual information within nearby scenes. Additionally, it introduces a Global Context Fusion Module (GCFM), facilitating the interaction and fusion of local and global features through self-attention mechanisms, significantly improving the model’s occluded target detection capabilities. An independent dataset comprising over 8000 outdoor images was collected for the purpose of evaluating AG-YOLO’s performance. After a meticulous selection process, a subset of 957 images meeting the criteria for occlusion scenarios of citrus fruits was obtained. This dataset includes instances of occlusion, severe occlusion, overlap, and severe overlap, covering a range of complex scenarios. AG-YOLO demonstrated exceptional performance on this dataset, achieving a precision (P) of 90.6%, a mean average precision (mAP)@50 of 83.2%, and an mAP@50:95 of 60.3%. These metrics surpass existing mainstream object detection methods, confirming AG-YOLO’s efficacy. AG-YOLO effectively addresses the challenge of occlusion detection, achieving a speed of 34.22 frames per second (FPS) while maintaining a high level of detection accuracy. This speed of 34.22 FPS showcases a relatively faster performance, particularly evident in handling the complexities posed by occlusion challenges, while maintaining a commendable balance between speed and accuracy. AG-YOLO, compared to existing models, demonstrates advantages in high localization accuracy, minimal missed detection rates, and swift detection speed, particularly evident in effectively addressing the challenges posed by severe occlusions in object detection. This highlights its role as an efficient and reliable solution for handling severe occlusions in the field of object detection.

An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.

Wheat exhibits complex characteristics during its growth, such as extensive tillering, slender and soft leaves, and severe organ cross-obscuration, posing a considerable challenge in full-cycle phenotypic monitoring. To address this, this study presents a synthesized method based on SFM-MVS (Structure-from-Motion, Multi-View Stereo) processing for handling and segmenting wheat point clouds, covering the entire growth cycle from seedling to grain filling stages. First, a multi-view image acquisition platform was constructed to capture image sequences of wheat plants, and dense point clouds were generated using SFM-MVS technology. High-quality dense point clouds were produced by implementing improved Euclidean clustering combined with centroids, color filtering, and statistical filtering methods. Subsequently, the segmentation of wheat plant stems and leaves was performed using the region growth segmentation algorithm. Although segmentation performance was suboptimal during the tillering, jointing, and booting stages due to the glut leaves and severe overlap, there was a salient improvement in wheat leaf segmentation efficiency over the entire growth cycle. Finally, phenotypic parameters were analyzed across different growth stages, comparing automated measurements of plant height, leaf length, and leaf width with actual measurements. The results demonstrated coefficients of determination ( ) of 0.9979, 0.9977, and 0.995; root mean square errors (RMSE) of 1.0773 cm, 0.2612 cm, and 0.0335 cm; and relative root mean square errors (RRMSE) of 2.1858%, 1.7483%, and 2.8462%, respectively. These results validate the reliability and accuracy of our proposed workflow in processing wheat point clouds and automatically extracting plant height, leaf length, and leaf width, indicating that our 3D reconstructed wheat model achieves high precision and can quickly, accurately, and non-destructively extract phenotypic parameters. Additionally, plant height, convex hull volume, plant surface area, and Crown area were extracted, providing a detailed analysis of dynamic changes in wheat throughout its growth cycle. ANOVA was conducted across different cultivars, accurately revealing significant differences at various growth stages. This study proposes a convenient, rapid, and quantitative analysis method, offering crucial technical support for wheat plant phenotypic analysis and growth dynamics monitoring, applicable for precise full-cycle phenotypic monitoring of wheat.

Analysis and interpretation of joint source separation and sound event detection in domestic environments.

In recent years, the relation between Sound Event Detection (SED) and Source Separation (SSep) has received a growing interest, in particular, with the aim to enhance the performance of SED by leveraging the synergies between both tasks. In this paper, we present a detailed description of JSS (Joint Source Separation and Sound Event Detection), our joint-training scheme for SSep and SED, and we measure its performance in the DCASE Challenge for SED in domestic environments. Our experiments demonstrate that JSS can improve SED performance, in terms of Polyphonic Sound Detection Score (PSDS), even without additional training data. Additionally, we conduct a thorough analysis of JSS's effectiveness across different event classes and in scenarios with severe event overlap, where it is expected to yield further improvements. Furthermore, we introduce an objective measure to assess the diversity of event predictions across the estimated sources, shedding light on how different training strategies impact the separation of sound events. Finally, we provide graphical examples of the Source Separation and Sound Event Detection steps, aiming to facilitate the interpretation of the JSS methods.

Commissural alignment during TAVR reduces the risk of overlap to coronary ostia

BackgroundTranscatheter aortic valve replacement (TAVR) showed safety and efficacy in patients with severe aortic stenosis. Commissural alignment (CA) during TAVR has the potential to reduce the impact of the prostheses on accessibility of coronary arteries, as misalignment of the neocommissures could cause partial overlap with coronary ostia. Therefore, the aim of this study was to investigate the impact of CA on coronary overlap rates. MethodsWe examined the techniques of CA and their impact on coronary access. Eligible studies were searched for on Pubmed, SCOPUS and DOAJ and selected using PRISMA guidelines. The primary endpoint was the incidence of a severe coronary overlap or failed coronary re-access. Results of the analysis are expressed as Risk Ratio (RR) with 95% CI. ResultsFour studies were included in this analysis. In these, 681 patients underwent TAVR with CA and 210 underwent TAVR without CA. We examined Evolut valves and Acurate Neo valves. The primary endpoint occurred in 138 patients undergoing TAVR with CA and in 154 patients without CA (RR = 0.279; 95% CI 0.201–0.386; p < 0.001). Neither prosthesis-related, nor patient-related factors had a significant interaction with the measured effect. ConclusionsCommissural alignment was associated with significantly lower rates of commissure-to-coronary ostia overlap and failure of coronary access. Consequently, a modified insertion technique could reduce coronary overlap and coronary occlusion, particularly in supra-annular valves. Therefore, controlled orientation of prostheses by CA during TAVR could favour coronary access, especially in younger patients that could require coronary re-access after TAVR.

Multiple paddy disease recognition methods based on deformable transformer attention mechanism in complex scenarios

Paddy disease recognition presents challenges in the agricultural industry, and existing algorithms struggle to accurately identify diseases in complex scenarios. In this paper, we propose a precise object detection framework to address the challenges of severe overlap, multi-disease detection, morphological irregularities, multi-scale object classification, and complex scenarios in real-world environments in paddy disease detection. The proposed model is based on an improved version of the DEtection TRansformer (Detr) algorithm. The enhanced network architecture fuses multi-scale features by adding a feature fusion module after the backbone network, which is able to retain more original information of the images and greatly improves the detection accuracy; the use of deformable attention module greatly reduces the computational complexity of the model. To evaluate the PDN, a dedicated paddy disease detection dataset with 1200 images is created. Experimental results demonstrate that the proposed model obtained a precision value of 100%, a recall value of 89.3%, F1-score of 94.3%, and a mean average precision (mAP) value of 60.2%. The model outperforms the existing state-of-the-art detection models in detection accuracy.

Elucidating state-specific dynamics of 1La and 1Lb in cyanoindole derivatives using UV/Vis pump IR detection

Indole possesses two close-lying excited states, namely 1La and 1Lb, which are difficult to distinguish using electronic spectroscopy due to the severe spectral overlap. Using UV/Vis pumped IR detection, we demonstrated that two well-separated excited-state vibrational bands are from the 1La and 1Lb states with the indole derivatives having CN substituted on the six-membered rings (4-7CNIs) in ethanol. We observed a population redistribution process between 1La and 1Lb with a time constant of ∼20 ps in 4-7CNIs, which is driven by the excitation-induced solvation relaxation in ethanol. In 3CNI, where the CN is attached on the five-membered ring, only a single peak corresponding to the 1Lb state was detected. Our work shows that with the prolonged nanosecond lifetime and enhanced oscillator strength compared to the ground-state CN stretch, the CN vibrational stretches on the excited states of CNIs can provide unique and benchmarking information on the state-specific dynamics and properties.

Ratiometric lysosome-targeting luminescent probe based on a coumarin-ruthenium(II) complex for formaldehyde detection and imaging in living cells and mouse brain tissues.

Ratiometric luminescence probes have attracted widespread attention because of their self-calibration capability. However, some defects, such as small emission shift, severe spectral overlap and poor water solubility, limit their application in the field of biological imaging. In this study, a unique luminescence probe, Ru-COU, has been developed by combining tris(bipyridine)ruthenium(II) complex with coumarin derivative through a formaldehyde-responsive linker. The probe exhibited a large emission shift (Δλ>100nm) and good water solubility, achieving ratiometric emission responses at 505nm and 610nm toward formaldehyde under acidic conditions. Besides, ratiometric luminescence imaging of formaldehyde in living cells and Alzheimer disease mouse's brain slices demonstrates the potential value of Ru-COU for the diagnosis and treatment of formaldehyde related diseases.

Rapid Measurement of Heteronuclear Coupling Constants in Complex NMR Spectra.

The NMR analysis of fluorine-containing molecules, increasingly widespread due to their importance in pharmaceuticals and biochemistry, poses significant challenges. Severe peak overlap in the proton spectrum often hinders the extraction of critical structural information in the form of heteronuclear scalar coupling constants, which are crucial for determining pharmaceutical properties and biological activity. Here, a new method, IPAP-FESTA, is reported that drastically simplifies measurements of the signs and magnitudes of proton-fluorine couplings. Its usefulness is demonstrated for the structural study of the steroidal drug fluticasone propionate extracted from a commercial formulation and for assessing solvent effects on the conformational equilibrium in a physically inseparable fluorohydrin mixture.

Smart Multivariate Spectrophotometric Determination of Two Co-Administered Autoimmune Drugs; Sulfasalazine and Pentoxifylline; in Bulk and Spiked Human Plasma.

Sulfasalazine and pentoxifylline are co-prescribed together to treat psoriasis and pemphigus vulgaris. Sulfasalazine is an anti-inflammatory, immunosuppressant, and antibiotic drug, while pentoxifylline is a vasodilator and immunosuppressant. The spectra of the two drugs and plasma suffer from severe overlap. This work aims to simultaneously determine sulfasalazine and pentoxifylline in their binary mixture and spiked human plasma by the assessment of their UV spectral data. Two model updated chemometric methods were established using principal component regression and partial least-squares regression models. The two models were validated in accordance with the U.S. Food and Drug Administration guidelines for bioanalysis and were applied for the determination of both drugs in synthetic mixtures or spiked human plasma. Accuracy and precision were within the accepted limits. In addition, three different assessment methods were used to evaluate the environmental greenness of the proposed models. The two updated models are simple, rapid, sensitive, and precise, and could be easily applied in QC laboratories for determination of sulfasalazine and pentoxifylline, without any preliminary separation steps or interference from plasma matrix. Two updated chemometric models called principlal component regression and partial least-squares regression were established for determination of sulfasalazine and pentoxifylline in spiked human plasma using UV spectrophotometric data.