Explore the vast range of titles available.

Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations.

Studies have challenged the appropriateness of accepted blood pressure targets. We hypothesised that different levels of low blood pressure are associated with benefit for some, but harm for other outcomes. In this analysis, we assessed the previously reported outcome data from high-risk patients aged 55 years or older with a history of cardiovascular disease, 70% of whom had hypertension, from the ONTARGET and TRANSCEND trials investigating ramipril, telmisartan, and their combination, with a median follow-up of 56 months. Detailed descriptions of randomisation and intervention have already been reported. We analysed the associations between mean blood pressure achieved on treatment; prerandomisation baseline blood pressure; or time-updated blood pressure (last on treatment value before an event) on the composite outcome of cardiovascular death, myocardial infarction, stroke, and hospital admission for heart failure; the components of the composite outcome; and all-cause death. Analysis was done by Cox regression analysis, ANOVA, and χ2. These trials were registered with ClinicalTrials.gov, number NCT00153101. Recruitment for ONTARGET took place between Dec 1, 2001, and July 31, 2008. TRANSCEND took place between Nov 1, 2001, and May 30, 2004. 30 937 patients were recruited from 733 centres in 40 countries and followed up for a median of 56 months. In ONTARGET, 25 127 patients known to be tolerant to angiotensin-converting-enzyme (ACE)-inhibitors were randomly assigned after a run-in period to oral ramipril 10 mg/day (n=8407), telmisartan 80 mg/day (n=8386), or the combination of both (n=8334). In TRANSCEND, 5810 patients who were intolerant to ACE-inhibitors were randomly assigned to oral telmisartan 80 mg/day (n=2903) or placebo (n=2907). Baseline systolic blood pressure (SBP) 140 mm Hg or higher was associated with greater incidence of all outcomes compared with 120 mm Hg to less than 140 mm Hg. By contrast, a baseline diastolic blood pressure (DBP) less than 70 mm Hg was associated with the highest risk for most outcomes compared with all DBP categories 70 mm Hg or more. In 4052 patients with SBP less than 120 mm Hg on treatment, the risk of the composite cardiovascular outcome (adjusted hazard ratio [HR] 1·14, 95% CI 1·03–1·26), cardiovascular death (1·29, 1·12–1·49), and all deaths (1·28, 1·15–1·42) were increased compared with those in whom SBP was 120–140 mm Hg during treatment (HR 1 for all outcomes, n=16099). No harm or benefit was observed for myocardial infarction, stroke, or hospital admission for heart failure. Mean achieved SBP more accurately predicted outcomes than baseline or time-updated SBP, and was associated with the lowest risk at approximately 130 mm Hg, and at 110–120 mm Hg risk increased for the combined outcome, cardiovascular death, and all-cause death except stroke. A mean DBP less than 70 mm Hg (n=5352) during treatment was associated with greater risk of the composite primary outcome (HR 1·31, 95% CI 1·20–1·42), myocardial infarction (1·55, 1·33–1·80), hospital admission for heart failure (1·59, 1·36–1·86) and all-cause death (1·16, 1·06–1·28) than a DBP 70–80 mm Hg (14 305). A pretreatment and mean on-treatment DBP of about 75 mm Hg was associated with the lowest risk. Mean achieved SBP less than 120 mm Hg during treatment was associated with increased risk of cardiovascular outcomes except for myocardial infarction and stroke. Similar patterns were observed for DBP less than 70 mm Hg, plus increased risk for myocardial infarction and hospital admission for heart failure. Very low blood pressure achieved on treatment was associated with increased risks of several cardiovascular disease events. These data suggest that the lowest blood pressure possible is not necessarily the optimal target for high-risk patients, although it is not possible to rule out some effect of reverse causality. Boehringer Ingelheim.

Linezolid plays a crucial role in the first-line treatment of drug-resistant tuberculosis globally. Its prolonged use can lead to neurological and haematological toxicity, highlighting the need for safer oxazolidinones. Delpazolid, a novel oxazolidinone, might be safer. We aimed to evaluate the safety and efficacy of delpazolid and identify an optimal dose. PanACEA-DECODE-01 was a prospective, randomised, open-label, phase 2b, multicentre, dose-finding trial done in five tuberculosis trial sites in Tanzania and South Africa. Adults aged 18–65 years, who weighed 40–90 kg, and had newly diagnosed, smear positive pulmonary tuberculosis were randomly assigned (1:1:1:1:1) through centralised allocation, using a probabilistic minimisation algorithm to receive no delpazolid (D0), delpazolid 400 mg once daily (D400), delpazolid 800 mg once daily (D800), delpazolid 1200 mg once daily (D1200), or delpazolid 800 mg twice daily (D800BD), all administered orally for 16 weeks with follow-up to week 52. All participants received bedaquiline (400 mg orally once daily for the first 14 days, then 200 mg orally thrice weekly), delamanid (100 mg orally twice daily), and moxifloxacin (400 mg orally once daily). Randomisation was stratified based on bacterial load in sputum as measured by GeneXpert cycle threshold (<16 vs ≥16), site, and HIV status. The primary efficacy objective was to establish an exposure–response model with the primary endpoint, measured in the modified intention-to-treat population, of change in mycobacterial load measured by time to positivity using the liquid culture mycobacterial growth indicator tube system. A secondary outcome was the time on treatment to sustained conversion to negative sputum culture in liquid media. The primary safety outcome was the occurrence of oxazolidinone class toxicities defined as peripheral or optical neuropathy, incident leukopenia, anaemia or thrombocytopenia, or adverse events in line with tyramine pressor response, all of grade 2 or higher, possibly, probably or definitely related to delpazolid. This study was registered with ClinicalTrials.gov, NCT04550832. Between Oct 28, 2021, and Aug 31, 2022, 156 individuals were screened for eligibility, 76 of whom were enrolled and randomly assigned to D0 (n=15), D400 (n=15), D800 (n=15), D1200 (n=16), or D800BD (n=15). 60 (79%) of 76 participants were male and 16 (21%) were female. Population pharmacokinetic–pharmacodynamic modelling suggests maximal microbiological activity at a daily total exposure of delpazolid (area under the concentration curve from 0 h to 24 h [AUC0–24]) of 50 mg/L per h; close to the median exposure observed after a 1200 mg dose. This maximal effect was estimated at a 38% (95% CI 4–83; p=0·025) faster decline in bacterial load compared with no delpazolid. In the secondary time-to-event analysis, there was no significant difference in time to culture conversion between treatment arms or exposure tertile. When all delpazolid-containing groups were combined, the hazard ratio for the time to sustained culture conversion to negative, comparing all delpazolid-containing groups with the group without delpazolid was 1·53 (95% CI 0·84–2·76). Two drug-related serious adverse events (one gastritis and one anaemia) occurred in the D800BD group, with high individual AUC0–24. Apart from the anaemia and one event of brief, moderate neutropenia observed at only one visit in the D800 group not in line with the characteristics of oxazolidinone class toxicity, no oxazolidinone class toxicities occurred. The pharmacokinetic–pharmacodynamic modelling results suggest that delpazolid adds efficacy on top of bedaquiline, delamanid, and moxifloxacin; and that a dose of 1200 mg once daily would result in exposures with maximum efficacy. That dose was shown to be safe, raising hope that linezolid toxicities could be averted in long-term treatment. Delpazolid is a promising drug for future tuberculosis treatment regimens and could be widely usable if safety and efficacy are confirmed in larger trials. LigaChem Biosciences, EDCTP2 programme supported by the EU; German Ministry for Education and Research; German Center for Infection Research; Swiss State Secretariat for Education, Research and Innovation; and Nederlandse Organisatie voor Wetenschappelijk Onderzoek.

Farnesoid X receptor (FXR) is a promising target for nonalcoholic steatohepatitis (NASH) and fibrosis. Although various FXR agonists have shown anti-fibrotic effects in diverse preclinical animal models, the response rate and efficacies in clinical trials were not optimum. Here we report that prophylactic but not therapeutic administration of obeticholic acid (OCA) prevents hepatic stellate cell (HSC) activation and fibrogenesis. Activated HSCs show limited response to OCA and other FXR agonists due to enhanced FXR SUMOylation. SUMOylation inhibitors rescue FXR signaling and thereby increasing the efficacy of OCA against HSC activation and fibrosis. FXR upregulates Perilipin-1 , a direct target gene of FXR, to stabilize lipid droplets and thereby prevent HSC activation. Therapeutic coadministration of OCA and SUMOylation inhibitors drastically impedes liver fibrosis induced by CCl 4 , bile duct ligation, and more importantly NASH. In conclusion, we propose a promising therapeutic approach by combining SUMOylation inhibitors and FXR agonists for liver fibrosis. FXR agonists have been investigated for the treatment of non-alcoholic steatohepatitis and liver fibrosis but the clinical efficacy is not optimal. Here the authors show that enhanced FXR SUMOylation in activated hepatic stellate cells reduces FXR signaling and that this can be rescued by SUMOylation inhibitors.

Large collections of time series often have aggregation constraints due to product or geographical groupings. The forecasts for the most disaggregated series are usually required to add-up exactly to the forecasts of the aggregated series, a constraint we refer to as \"coherence.\" Forecast reconciliation is the process of adjusting forecasts to make them coherent. The reconciliation algorithm proposed by Hyndman et al. ( 2011 ) is based on a generalized least squares estimator that requires an estimate of the covariance matrix of the coherency errors (i.e., the errors that arise due to incoherence). We show that this matrix is impossible to estimate in practice due to identifiability conditions. We propose a new forecast reconciliation approach that incorporates the information from a full covariance matrix of forecast errors in obtaining a set of coherent forecasts. Our approach minimizes the mean squared error of the coherent forecasts across the entire collection of time series under the assumption of unbiasedness. The minimization problem has a closed-form solution. We make this solution scalable by providing a computationally efficient representation. We evaluate the performance of the proposed method compared to alternative methods using a series of simulation designs which take into account various features of the collected time series. This is followed by an empirical application using Australian domestic tourism data. The results indicate that the proposed method works well with artificial and real data. Supplementary materials for this article are available online.

The polymicrobial nature of ventilator-associated pneumonia (VAP) is now evident, with mixed bacterial-fungal biofilms colonizing the VAP endotracheal tube (ETT) surface. The microbial interplay within this infection may contribute for enhanced pathogenesis and exert impact towards antimicrobial therapy. Consequently, the high mortality/morbidity rates associated to VAP and the worldwide increase in antibiotic resistance has promoted the search for novel therapeutic strategies to fight VAP polymicrobial infections. Under this scope, this work aimed to assess the activity of mono- vs combinational antimicrobial therapy using one antibiotic (Polymyxin B; PolyB) and one antifungal (Amphotericin B; AmB) agent against polymicrobial biofilms of Pseudomonas aeruginosa and Candida albicans. The action of isolated antimicrobials was firstly evaluated in single- and polymicrobial cultures, with AmB being more effective against C. albicans and PolyB against P. aeruginosa. Mixed planktonic cultures required equal or higher antimicrobial concentrations. In biofilms, only PolyB at relatively high concentrations could reduce P. aeruginosa in both monospecies and polymicrobial populations, with C. albicans displaying only punctual disturbances. PolyB and AmB exhibited a synergistic effect against P. aeruginosa and C. albicans mixed planktonic cultures, but only high doses (256 mg L-1) of PolyB were able to eradicate polymicrobial biofilms, with P. aeruginosa showing loss of cultivability (but not viability) at 2 h post-treatment, whilst C. albicans only started to be inhibited after 14 h. In conclusion, combination therapy involving an antibiotic and an antifungal agent holds an attractive therapeutic option to treat severe bacterial-fungal polymicrobial infections. Nevertheless, optimization of antimicrobial doses and further clinical pharmacokinetics/pharmacodynamics and toxicodynamics studies underpinning the optimal use of these drugs are urgently required to improve therapy effectiveness and avoid reinfection.

Evolved resistance to one antibiotic may be associated with \"collateral\" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.

Background Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs. Results In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments. Conclusion This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

Linezolid is a key component globally in first-line therapy for drug-resistant tuberculosis but has considerable toxicity. New and safer alternative oxazolidinones are needed. Sutezolid is one such promising alternative. We aimed to evaluate preliminary efficacy and safety of sutezolid and to identify an optimal dose. PanACEA-SUDOCU-01 was a prospective, open-label, randomised, phase 2b dose-finding study in four tuberculosis trial sites in Tanzania and South Africa. Adults aged 18–65 years with newly diagnosed, drug-sensitive, smear-positive tuberculosis were enrolled and randomly assigned (1:1:1:1:1) by a probabilistic minimisation algorithm using a web-based interface, stratified by site, sex, and HIV status, to receive no sutezolid (U0), sutezolid 600 mg once daily (U600), sutezolid 1200 mg once daily (U1200), sutezolid 600 mg twice daily (U600BD), or sutezolid 800 mg twice daily (U800BD), all administered orally for 12 weeks followed by standard therapy for 6 months. All participants received oral bedaquiline (400 mg once daily for 14 days followed by 200 mg thrice weekly), oral delamanid (100 mg twice daily), and oral moxifloxacin (400 mg once daily). For the primary endpoint, measured in the modified intention-to-treat population, sputum samples were taken weekly to measure the change in bacterial load measured by time to positivity using the mycobacterial growth indicator tube system. Safety was assessed through weekly electrocardiography, safety blood tests, vision testing, and physical and neurological examinations. Intensive pharmacokinetic measurements were done on day 14 to determine exposure to sutezolid, bedaquiline, delamanid, and moxifloxacin. This trial is registered with ClinicalTrials.gov (NCT03959566). Between May 20, 2021, and Feb 17, 2022, 186 individuals were screened for eligibility, 75 of whom were enrolled and randomly assigned to U0 (n=16), U600 (n=15), U1200 (n=14), U600BD (n=15), or U800BD (n=15). 56 (75%) participants were male and 19 (25%) were female. The final pharmacokinetic–pharmacodynamic model showed a benefit of sutezolid, with an increase in time to positivity slope steepness of 16·7% (95% CI 0·7–35·0) at the maximum concentration typical for the 1200 mg dose, compared with no sutezolid exposure. A maximum effect of sutezolid exposure was not observed within the investigated dose range. Six (8%) participants (one in the U600 group, two in the U600BD group, one in the U800BD group, and two retrospectively identified in the U600 group) had an increase in a QT interval using Fridericia correction greater than 60 ms from baseline. Two (3%) participants in the U600BD group had grade 4 adverse events, one each of neutropenia and hepatotoxicity, but they were not deemed associated with the use of sutezolid by the investigators. No neuropathy was reported. Sutezolid, combined with bedaquiline, delamanid, and moxifloxacin, was shown to be efficacious and added activity to the background drug combination, although we cannot make a final dose recommendation yet. This study provides valuable information for the selection of sutezolid doses for future studies, and described no oxazolidinone class toxicities at the doses used. EDCTP2 programme funded by the EU; German Ministry for Education and Research; German Center for Infection Research; and Nederlandse Organisatie voor Wetenschappelijk Onderzoek.

Background Combination therapies involving novel agents, such as immunotherapies and targeted therapies, offer significant antitumor benefits by increasing dose intensity, targeting multiple pathways, and benefiting a broader patient population. To further explore these advantages, the National Cancer Institute (NCI) has initiated Combination Therapy Platform Trial with Molecular Analysis for Therapy Choice (ComboMATCH) to evaluate the effectiveness of new drug combinations in treating both adults and children. However, designing dose optimization trials for these combination therapies presents substantial challenges due to the complex interactions and unique mechanisms of action. Methods To address these challenges, we propose COMPACT, a Bayesian phase I-II randomized design for combination cancer therapies that uses progression-free survival (PFS) as the primary efficacy endpoint to identify the optimal dose combination (ODC) based on restricted mean survival time (RMST). The COMPACT design jointly evaluates both toxicity and PFS, with continuous toxicity monitoring throughout the trial. Toxicity probabilities are modeled using a partial ordering assumption without relying on complex parametric models, while PFS is modeled through a Bayesian Pareto proportional hazards model with gamma-shared frailty. The trial consists of two seamlessly connected stages. In the first stage, the dose space is explored primarily based on toxicity, while PFS data are concurrently collected. In the second stage, patients are adaptively randomized to safe and potentially promising dose combinations based on PFS, and the dose combination with the highest RMST among those deemed safe is selected as the ODC. Results Simulation studies demonstrate that COMPACT has desirable operating characteristics and outperforms conventional designs in identifying the ODC, allocating more patients to ODC, while maintaining patient safety. Sensitivity analysis is performed to examine the robustness of the proposed design. A trial example is provided to facilitate the practical implementation of the proposed COMPACT design. Conclusions The proposed COMPACT design offers a novel and robust framework for combination cancer therapies with progression-free survival end point.