High-density lipoproteins (HDL) are micelle-like particles consisting of a core of triglycerides and cholesteryl esters surrounded by a shell of phospholipid, cholesterol, and apolipoproteins. HDL is considered \"good\" cholesterol, and its concentration in plasma is used clinically in assessing cardiovascular health. However, these particles vary in structure, composition, and therefore function, and thus can be resolved into subpopulations, some of which have specific cardioprotective properties. Mass measurements of HDL by charge detection mass spectrometry (CD-MS) previously revealed seven distinct subpopulations which could be delineated by mass and charge [Lutomski, C. A. et al. Anal. Chem. 2018]. Here, we investigate the thermal stabilities of these subpopulations; upon heating, the particles within each subpopulation undergo structural rearrangements with distinct transition temperatures. In addition, we find evidence for many new families of structures within each subpopulation; at least 15 subspecies of HDL are resolved. These subspecies vary in size, charge, and thermal stability. While this suggests that these new subspecies have unique molecular compositions, we cannot rule out the possibility that we have found evidence for new structural forms within the known subpopulations. The ability to resolve new subspecies of HDL particles may be important in understanding and delineating the role of unique particles in cardiovascular health and disease.

The melt analysis feature in most real-time polymerase chain reaction (PCR) instruments is a simple method for determining if expected or unexpected products are present. High-resolution melt (HRM) analysis seeks to improve the precision of melt temperature measurements for better PCR product sequence characterization. In the area of tuberculosis (TB) drug susceptibility screening, sequencing has shown that a single base change can be sufficient to make a first-line TB drug ineffective. In this study, a reagent-based calibration strategy based on synthetic left-handed (L)-DNA, designated LHRM, was developed to confirm validation of a PCR product with single base resolution. To test this approach, a constant amount of a double-stranded L-DNA melt comparator was added to each sample and used as a within-sample melt standard. The performance of LHRM and standard HRM was used to classify PCR products as drug-susceptible or not drug-susceptible with a test bed of nine synthetic katG variants, each containing single or multiple base mutations that are known to confer resistance to the first-line TB drug isoniazid (INH). LHRM achieved comparable classification to standard HRM relying only on within-sample melt differences between L-DNA and the unknown PCR product. Using a state-of-the-art calibrated instrument and multiple sample classification analysis, standard HRM was performed at 66.7% sensitivity and 98.8% specificity. Single sample analysis incorporating L-DNA for reagent-based calibration into every sample maintained high performance at 77.8% sensitivity and 98.7% specificity. LHRM shows promise as a high-resolution single sample method for validating PCR products in applications where the expected sequence is known.

As water miscible organic co-solvents are often required for enzyme reactions to improve e.g., the solubility of the substrate in the aqueous medium, an enzyme is required which displays high stability in the presence of this co-solvent. Consequently, it is of utmost importance to identify the most suitable enzyme or the appropriate reaction conditions. Until now, the melting temperature is used in general as a measure for stability of enzymes. The experiments here show, that the melting temperature does not correlate to the activity observed in the presence of the solvent. As an alternative parameter, the concentration of the co-solvent at the point of 50% protein unfolding at a specific temperature T in short c U 50 T is introduced. Analyzing a set of ene reductases, c U 50 T is shown to indicate the concentration of the co-solvent where also the activity of the enzyme drops fastest. Comparing possible rankings of enzymes according to melting temperature and c U 50 T reveals a clearly diverging outcome also depending on the specific solvent used. Additionally, plots of c U 50 versus temperature enable a fast identification of possible reaction windows to deduce tolerated solvent concentrations and temperature.

Silver iodide is a prototype compound of superionic conductors that allows ions to flow through its structure. It exhibits a first-order phase transition at 420 K, characterized by an abrupt change in its ionic conductivity behavior, and above this temperature, its ionic conductivity increases by more than three orders of magnitude. Introducing small concentrations of carbon into the silver iodide structure produces a new material with a mixed conductivity (ionic and electronic) that increases with increasing temperature. In this work, we report the experimental results of the ionic conductivity as a function of the reciprocal temperature for the (AgI)x - C(1-x) mixture at low carbon concentrations (x = 0.99, 0.98, and 0.97). The ionic conductivity behavior as a function of reciprocal temperature was well fitted using a phenomenological model based on a random variable theory with a probability distribution function for the carriers. The experimental data show a proximity effect between the C and AgI phases. As a consequence of this proximity behavior, carbon concentration or temperature can control the conductivity of the (AgI)x - C(1-x) mixture.

The Non-tuberculous mycobacterial (NTM) isolates should be distinguished from tuberculosis and identified at the species level for choosing an appropriate treatment plan. In this study, two molecular methods were used to differentiate NTM species, including a new designed High Resolution Melting (HRM) and Multilocus Sequence Analysis (MLSA). Seventy-five mycobacterial isolates were evaluated by sequencing four genes ( MLSA) and a HRM assay specifically targeting atpE was designed to rapidly and accurately identify and differentiate mycobacterium species. Out of 70 NTM isolates, 66 (94.3%), 65 (92.9%), 65 (92.9%) and 64 (91.4%) isolates were identified to the species level by PCR of atpE, tuf, rpoB and dnaK genes. We could identify 100% of the isolates to the species level (14 different species) by MLSA. By using HRM assay, all NTM isolates were identified and classified into eight groups, in addition, Mycobacterium tuberculosis and Nocardia were also detected simultaneously. The MLSA technique was able to differentiate all 14 species of NTM isolates. According to the results, the HRM assay is a rapid and beneficial method for identifying NTM, M. tuberculosis (MTB), and Nocardia isolates without sequencing.

Sucrose and trehalose pharmaceutical excipients are employed to stabilize protein therapeutics in a dried state. The mechanism of therapeutic protein stabilization is dependent on the sugars being present in an amorphous solid-state. Colyophilization of sugars with high glass transition polymers, polyvinylpyrrolidone (PVP), and poly(vinylpyrrolidone vinyl acetate) (PVPVA), enhances amorphous sugar stability. This study investigates the stability of colyophilized sugar-polymer systems in the frozen solution state, dried state postlyophilization, and upon exposure to elevated humidity. Binary systems of sucrose or trehalose with PVP or PVPVA were lyophilized with sugar/polymer ratios ranging from 2:8 to 8:2. Frozen sugar-PVPVA solutions exhibited a higher glass transition temperature of the maximally freeze-concentrated amorphous phase (Tg\') compared to sugar-PVP solutions, despite the glass transition temperature (Tg) of PVPVA being lower than PVP. Tg values of all colyophilized systems were in a similar temperature range irrespective of polymer type. Greater hydrogen bonding between sugars and PVP and the lower hygroscopicity of PVPVA influenced polymer antiplasticization effects and the plasticization effects of residual water. Plasticization due to water sorption was investigated in a dynamic vapor sorption humidity ramping experiment. Lyophilized sucrose systems exhibited increased amorphous stability compared to trehalose upon exposure to the humidity. Recrystallization of trehalose was observed and stabilized by polymer addition. Lower concentrations of PVP inhibited trehalose recrystallization compared to PVPVA. These stabilizing effects were attributed to the increased hydrogen bonding between trehalose and PVP compared to trehalose and PVPVA. Overall, the study demonstrated how differences in polymer hygroscopicity and hydrogen bonding with sugars influence the stability of colyophilized amorphous dispersions. These insights into excipient solid-state stability are relevant to the development of stabilized biopharmaceutical solid-state formulations.

Differential scanning fluorimetry (DSF) is a method to determine the apparent melting temperature (Tma) of a purified protein. In DSF, the raw unfolding curves from which Tma is calculated vary widely in shape and complexity. However, the tools available for calculating Tma are only compatible with the simplest of DSF curves, hindering many otherwise straightforward applications of the technology. To overcome this limitation, we designed new mathematical models for Tma calculation that accommodate common forms of variation in DSF curves, including the number of transitions, the presence of high initial signal, and temperature-dependent signal decay. When tested these models against DSFbase, an open-source database of 6235 raw, real-life DSF curves, these models outperformed the existing standard approaches of sigmoid fitting and maximum of the first derivative. To make these models accessible, we created an open-source software and website, DSFworld (https://gestwickilab.shinyapps.io/dsfworld/). In addition to these improved fitting capabilities, DSFworld also includes features that overcome the practical limitations of many analysis workflows, including automatic reformatting of raw data exported from common qPCR instruments, labeling of data based on experimental variables, and flexible interactive plotting. We hope that DSFworld will enable more streamlined and accurate calculation of Tma values for DSF experiments.

Identifying pathogenic microorganisms as early as possible is critical for selecting the appropriate antimicrobial therapy in infected patients. We previously reported the development of the Tm mapping method for identifying a broad range of pathogenic bacteria within 3 h of blood collection. However, the Tm mapping identification requires an analytical instrument with a tube-to-tube variation of no more than 0.1 °C, so we can only use a few instruments that have such high thermal accuracy. To address the problem, we developed the improved Tm mapping method using imperfect-match linear long quenching probes (IMLL Q-probes). Using IMLL Q-probes, almost all commercially available analytical instruments can be used for the Tm mapping method. Some bacterial species cannot be narrowed down to one species, but they can at least be narrowed down to the genus level. The Tm mapping method using IMLL Q-probes is useful for deciding on antimicrobial therapy in infected patients.

Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM\'s speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.
OBJECTIVE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.