Advancing Equity In The Pandemic Treaty

There is a broad consensus around equity’s importance. Even countries that hoarded supplies during the acute phase of COVID-19 seem to understand that the international community must find a means to ensure fairer allocation of medical resources when the next health crisis hits. But there has been little agreement about the concrete steps needed to operationalize fairer access and benefit sharing. That is, what are the workable mechanisms that could reduce the divide between richer and poorer populations? The World Health Assembly, the governing body of the World Health Organization, has appointed an Intergovernmental Negotiating Body to develop a pandemic convention, agreement, or other instrument under the WHO constitution. The February 2023 draft is designed “to achieve greater equity … through the fullest national and international cooperation.” It is important that the negotiators develop specific, measurable metrics that directly impact equity. The mechanisms and metrics agreed upon should allow the public to evaluate whether a more equitable system is emerging through this new regime. Equity won’t just happen. We need to plan and prepare for equity, and we need international norms with which nations must comply to achieve the fairness we strive for

Photodesorption of water ice: a molecular dynamics study

Absorption of ultraviolet radiation by water ice coating interstellar grains can lead to dissociation and desorption of the ice molecules. These processes are thought to be important in the gas-grain chemistry in molecular clouds and protoplanetary disks, but very few quantitative studies exist. We compute the photodesorption efficiencies of amorphous water ice and elucidate the mechanisms by which desorption occurs. Classical molecular dynamics calculations were performed for a compact amorphous ice surface at 10 K thought to be representative of interstellar ice. Dissociation and desorption of H2O molecules in the top six monolayers are considered following absorption into the first excited electronic state with photons in the 1300-1500 Angstrom range. The trajectories of the H and OH photofragments are followed until they escape or become trapped in the ice. The probability for H2O desorption per absorbed UV photon is 0.5-1% in the top three monolayers, then decreases to 0.03% in the next two monolayers, and is negligible deeper into the ice. The main H2O removal mechanism in the top two monolayers is through separate desorption of H and OH fragments. Removal of H2O molecules from the ice, either as H2O itself or its products, has a total probability of 2-3% per absorbed UV photon in the top two monolayers. In the third monolayer the probability is about 1% and deeper into the ice the probability of photodesorption falling to insignificant numbers. The probability of any removal of H2O per incident photon is estimated to be 3.7x10^-4, with the probability for photodesorption of intact H2O molecules being 1.4x10^-4 per incident photon. When no desorption occurs, the H and OH products can travel up to 70 and 60 Angstroms inside or on top of the surface during which they can react with other species.Comment: 12 pages, 10 figures, A&A, in pres

Safety and efficacy of hydroxychloroquine as prophylactic against COVID-19 in healthcare workers: a meta-analysis of randomised clinical trials

OBJECTIVE: We studied the safety and efficacy of hydroxychloroquine (HCQ) as pre-exposure prophylaxis for COVID-19 in healthcare workers (HCWs), using a meta-analysis of randomised controlled trials (RCTs). DATA SOURCES: PubMed and EMBASE databases were searched to identify randomised trials studying HCQ. STUDY SELECTION: Ten RCTs were identified (n=5079 participants). DATA EXTRACTION AND SYNTHESIS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in this systematic review and meta-analysis between HCQ and placebo using a Bayesian random-effects model. A pre-hoc statistical analysis plan was written. MAIN OUTCOMES: The primary efficacy outcome was PCR-confirmed SARS-CoV-2 infection and the primary safety outcome was incidence of adverse events. The secondary outcome included clinically suspected SARS-CoV-2 infection. RESULTS: Compared with placebo, HCWs randomised to HCQ had no significant difference in PCR-confirmed SARS-CoV-2 infection (OR 0.92, 95% credible interval (CI): 0.58, 1.37) or clinically suspected SARS-CoV-2 infection (OR 0.78, 95% CI: 0.57, 1.10), but significant difference in adverse events (OR 1.35, 95% CI: 1.03, 1.73). CONCLUSIONS AND RELEVANCE: Our meta-analysis of 10 RCTs investigating the safety and efficacy of HCQ as pre-exposure prophylaxis in HCWs found that compared with placebo, HCQ does not significantly reduce the risk of confirmed or clinically suspected SARS-CoV-2 infection, while HCQ significantly increases adverse events. PROSPERO REGISTRATION NUMBER: CRD42021285093

Laboratory evidence for efficient water formation in interstellar ices

Even though water is the main constituent in interstellar icy mantles, its chemical origin is not well understood. Three different formation routes have been proposed following hydrogenation of O, O2, or O3, but experimental evidence is largely lacking. We present a solid state astrochemical laboratory study in which one of these routes is tested. For this purpose O2 ice is bombarded by H- or D-atoms under ultra high vacuum conditions at astronomically relevant temperatures ranging from 12 to 28 K. The use of reflection absorption infrared spectroscopy (RAIRS) permits derivation of reaction rates and shows efficient formation of H2O (D2O) with a rate that is surprisingly independent of temperature. This formation route converts O2 into H2O via H2O2 and is found to be orders of magnitude more efficient than previously assumed. It should therefore be considered as an important channel for interstellar water ice formation as illustrated by astrochemical model calculations.Comment: 15 pages, 4 figures. ApJ, in pres

Mammography screening: views from women and primary care physicians in Crete

Background: Breast cancer is the most commonly diagnosed cancer among women and a leading cause of death from cancer in women in Europe. Although breast cancer incidence is on the rise worldwide, breast cancer mortality over the past 25 years has been stable or decreasing in some countries and a fall in breast cancer mortality rates in most European countries in the 1990s was reported by several studies, in contrast, in Greece have not reported these favourable trends. In Greece, the age-standardised incidence and mortality rate for breast cancer per 100.000 in 2006 was 81,8 and 21,7 and although it is lower than most other countries in Europe, the fall in breast cancer mortality that observed has not been as great as in other European countries. There is no national strategy for screening in this country. This study reports on the use of mammography among middleaged women in rural Crete and investigates barriers to mammography screening encountered by women and their primary care physicians. Methods: Design: Semi-structured individual interviews. Setting and participants: Thirty women between 45–65 years of age, with a mean age of 54,6 years, and standard deviation 6,8 from rural areas of Crete and 28 qualified primary care physicians, with a mean age of 44,7 years and standard deviation 7,0 serving this rural population. Main outcome measure: Qualitative thematic analysis. Results: Most women identified several reasons for not using mammography. These included poor knowledge of the benefits and indications for mammography screening, fear of pain during the procedure, fear of a serious diagnosis, embarrassment, stress while anticipating the results, cost and lack of physician recommendation. Physicians identified difficulties in scheduling an appointment as one reason women did not use mammography and both women and physicians identified distance from the screening site, transportation problems and the absence of symptoms as reasons for non-use. Conclusion: Women are inhibited from participating in mammography screening in rural Crete. The provision of more accessible screening services may improve this. However physician recommendation is important in overcoming women's inhibitions. Primary care physicians serving rural areas need to be aware of barriers preventing women from attending mammography screening and provide women with information and advice in a sensitive way so women can make informed decisions regarding breast caner screening

An Adaptive Sublinear-Time Block Sparse Fourier Transform

The problem of approximately computing the $k$ dominant Fourier coefficients of a vector $X$ quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in algorithms with $O(k\log n\log (n/k))$ runtime [Hassanieh \emph{et al.}, STOC'12] and $O(k\log n)$ sample complexity [Indyk \emph{et al.}, FOCS'14]. These results are proved using non-adaptive algorithms, and the latter $O(k\log n)$ sample complexity result is essentially the best possible under the sparsity assumption alone: It is known that even adaptive algorithms must use $\Omega((k\log(n/k))/\log\log n)$ samples [Hassanieh \emph{et al.}, STOC'12]. By {\em adaptive}, we mean being able to exploit previous samples in guiding the selection of further samples. This paper revisits the sparse FFT problem with the added twist that the sparse coefficients approximately obey a $(k_0,k_1)$-block sparse model. In this model, signal frequencies are clustered in $k_0$ intervals with width $k_1$ in Fourier space, and $k= k_0k_1$ is the total sparsity. Signals arising in applications are often well approximated by this model with $k_0\ll k$. Our main result is the first sparse FFT algorithm for $(k_0, k_1)$-block sparse signals with a sample complexity of $O^*(k_0k_1 + k_0\log(1+ k_0)\log n)$ at constant signal-to-noise ratios, and sublinear runtime. A similar sample complexity was previously achieved in the works on {\em model-based compressive sensing} using random Gaussian measurements, but used $\Omega(n)$ runtime. To the best of our knowledge, our result is the first sublinear-time algorithm for model based compressed sensing, and the first sparse FFT result that goes below the $O(k\log n)$ sample complexity bound. Interestingly, the aforementioned model-based compressive sensing result that relies on Gaussian measurements is non-adaptive, whereas our algorithm crucially uses {\em adaptivity} to achieve the improved sample complexity bound. We prove that adaptivity is in fact necessary in the Fourier setting: Any {\em non-adaptive} algorithm must use $\Omega(k_0k_1\log \frac{n}{k_0k_1})$ samples for the $(k_0,k_1$)-block sparse model, ruling out improvements over the vanilla sparsity assumption. Our main technical innovation for adaptivity is a new randomized energy-based importance sampling technique that may be of independent interest

Comparative Survival of Asian and White Metastatic Castration-Resistant Prostate Cancer Men Treated With Docetaxel

There are few data regarding disparities in overall survival (OS) between Asian and white men with metastatic castration-resistant prostate cancer (mCRPC). We compared OS of Asian and white mCRPC men treated in phase III clinical trials with docetaxel and prednisone (DP) or a DP-containing regimen. Individual participant data from 8820 men with mCRPC randomly assigned on nine phase III trials to receive DP or a DP-containing regimen were combined. Men enrolled in these trials had a diagnosis of prostate adenocarcinoma. The median overall survival was 18.8 months (95% confidence interval [CI] = 17.4 to 22.1 months) and 21.2 months (95% CI = 20.8 to 21.7 months) for Asian and white men, respectively. The pooled hazard ratio for death for Asian men compared with white men, adjusted for baseline prognostic factors, was 0.95 (95% CI = 0.84 to 1.09), indicating that Asian men were not at increased risk of death. This large analysis showed that Asian men did not have shorter OS duration than white men treated with docetaxel

Predictive factors for skeletal complications in hormone-refractory prostate cancer patients with metastatic bone disease

Factors predictive of skeletal-related events (SREs) in bone metastatic prostate cancer patients with hormone-refractory disease were investigated. We evaluated the frequency of SREs in 200 hormone-refractory patients consecutively observed at our Institution and followed until death or the last follow-up. Baseline parameters were evaluated in univariate and multivariate analysis as potential predictive factors of SREs. Skeletal-related events were observed in 86 patients (43.0%), 10 of which (5.0%) occurred before the onset of hormone-refractory disease. In univariate analysis, patient performance status (P=0.002), disease extent (DE) in bone (P=0.0001), bone pain (P=0.0001), serum alkaline phosphatase (P=0.0001) and urinary N-telopeptide of type one collagen (P=0.0001) directly correlated with a greater risk to develop SREs, whereas Gleason score at diagnosis, serum PSA, Hb, serum albumin, serum calcium, types of bone lesions and duration of androgen deprivation therapy did not. Both DE in bone (hazard ratio (HR): 1.16, 95% confidence interval (CI): 1.07–1.25, P=0.000) and pain score (HR: 1.13, 95% CI: 1.06–1.20, P=0.000) were independent variables predicting for the onset of SREs in multivariate analysis. In patients with heavy tumour load in bone and great bone pain, the percentage of SREs was almost twice as high as (26 vs 52%, P<0.02) and occurred significantly earlier (P=0.000) than SREs in patients with limited DE in bone and low pain. Bone pain and DE in bone independently predict the occurrence of SREs in bone metastatic prostate cancer patients with hormone-refractory disease. These findings could help physicians in tailoring the skeletal follow-up most appropriate to individual patients and may prove useful for stratifying patients enrolled in bisphosphonate clinical trials

Generation and characterisation of Friedreich ataxia YG8R mouse fibroblast and neural stem cell models

This article has been made available through the Brunel Open Access Publishing Fund.Background: Friedreich ataxia (FRDA) is an autosomal recessive neurodegenerative disease caused by GAA repeat expansion in the first intron of the FXN gene, which encodes frataxin, an essential mitochondrial protein. To further characterise the molecular abnormalities associated with FRDA pathogenesis and to hasten drug screening, the development and use of animal and cellular models is considered essential. Studies of lower organisms have already contributed to understanding FRDA disease pathology, but mammalian cells are more related to FRDA patient cells in physiological terms. Methodology/Principal Findings: We have generated fibroblast cells and neural stem cells (NSCs) from control Y47R mice (9 GAA repeats) and GAA repeat expansion YG8R mice (190+120 GAA repeats). We then differentiated the NSCs in to neurons, oligodendrocytes and astrocytes as confirmed by immunocytochemical analysis of cell specific markers. The three YG8R mouse cell types (fibroblasts, NSCs and differentiated NSCs) exhibit GAA repeat stability, together with reduced expression of frataxin and reduced aconitase activity compared to control Y47R cells. Furthermore, YG8R cells also show increased sensitivity to oxidative stress and downregulation of Pgc-1α and antioxidant gene expression levels, especially Sod2. We also analysed various DNA mismatch repair (MMR) gene expression levels and found that YG8R cells displayed significant reduction in expression of several MMR genes, which may contribute to the GAA repeat stability. Conclusions/Significance: We describe the first fibroblast and NSC models from YG8R FRDA mice and we confirm that the NSCs can be differentiated into neurons and glia. These novel FRDA mouse cell models, which exhibit a FRDA-like cellular and molecular phenotype, will be valuable resources to further study FRDA molecular pathogenesis. They will also provide very useful tools for preclinical testing of frataxin-increasing compounds for FRDA drug therapy, for gene therapy, and as a source of cells for cell therapy testing in FRDA mice. © 2014 Sandi et al

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics